AI papers — 2026-08-20

Problem: Existing embodied-agent harnesses remain open-loop — they follow fixed skills or preplanned trajectories during rollout and reflect only after an episode completes, so they cannot govern physical execution at the millisecond-level frequency needed to track rapidly changing robot-environment states.

Method: Zetta keeps the base VLA/WAM policy frozen and evolves a harness H = {C, R, T} (runtime Critics, Recovery playbooks, heterogeneous Toolset) through three timescale-separated loops: a Critic-Governed Action Loop that runs critics at action frequency and triggers recovery skills; a Rollout-Batch Candidate Optimization Loop that clusters failures via Earliest Observable Divergence and Top-Down Hierarchical Causal Diagnosis (across Evaluation → Critic → State → Planning → Recovery → Parameter layers), then proposes candidate patches using an SGD-like bounded code-space optimization drawing on SkillOpt and EmbodiSkill; and a Validation-Gated Skill Update Loop that admits only patches passing closed-loop and held-out generalization validation. This is paired with Z-Infra, a Ray-based rollout infrastructure with a Control Plane, Environment Worker layer, and Rollout Worker layer that decouples agent logic from hardware via batched inference, model partitioning (splitting the VLM and Action Expert of π0.5 into separate processes linked by CUDA IPC), and quantization (W8A8/W4A16/W4A8). Experiments use π0.5 as the frozen policy on LIBERO-Pro and GR00T N1.5 on RoboCasa, run on 8× RTX 4090 GPUs.

Key results:

• State-of-the-art success under current rollout budget: 90.8% on LIBERO-Pro and 93.6% on RoboCasa
• LIBERO-Pro success scales from 34.5% to 90.8% and RoboCasa from 73.6% to 93.6% across evolution iterations
• RoboCasa 18-task macro-average success rises 73.56% → 78.71% → 84.85% → 90.54% → 93.56% (+20.00 pts) over four cumulative repair rounds
• LIBERO-Pro Goal-T average SR: 31% → 92.5% (185/200 final); Goal-S average SR: 38% → 89% (178/200 final)
• Zero-shot skill transfer from PnP-Stove: success improves from 58% to 82% on PnP-Sink, 62% to 80% on PnP-Cabinet, and 72% to 90% on PnP-Toaster
• "Aha moments": 15% → 95% on Wine Bottle in Bowl, 5% → 90% on Put Cream Cheese on Bowl
• Zetta inference latency decreases by 91% vs. RPent (11.1× speedup)
• Z-Infra raises valid rollout throughput from 1.7 to 35.1 episodes/min (20.6× improvement)
• Model partitioning (VLM/AE split with CUDA IPC) cuts average inference latency by 53% and improves goodput (200ms SLO) by 2.4× vs. monolithic deployment; W8A8 quantization of prefix MLP modules gives 1.18×–1.32× speedup with no LIBERO success-rate degradation

Why it matters / caveats: By keeping the base policy frozen and evolving code-based critics/recoveries instead, Zetta turns rollout throughput into the scaling lever for physical intelligence and shows learned skills generalize zero-shot across related tasks, suggesting a scaling path distinct from collecting more demonstration data. Results are reported on simulation benchmarks (LIBERO-Pro, RoboCasa) with specific base policies (π0.5, GR00T N1.5) under a bounded rollout budget; the extracted text does not confirm real-world robot validation.

Problem: Prior LLM-based PLC (programmable logic controller) code generation systems evaluate mostly on isolated program organization units (POUs) and demonstrate—rather than measure—project integration and runtime behavior, so it is unknown how reliably generated control logic actually integrates into a real project and runs correctly.

Method: SEMAPLC is a model-agnostic, verification-gated agent harness built on a generic event-driven tool-use core, exposing a shared PLC MCP (Model Context Protocol) tool layer for syntax checking, compilation, deployment, live variable reading/forcing, and trace sampling. It grounds generation in either a local POU interface or an existing PLC project (reusing modules, variables, function blocks, and conventions), applies a PLC skill library (rules files, a curated wiki, procedural skills) encoding IEC 61131-3 practices, and gates task completion on three logged, external verification sources: a structured specification audit, compilation, and live-runtime validation against injected scenarios/golden traces (Algorithm 1), with bounded retries (r=2), edit invalidation of prior verdicts, and "earned claims" cross-checked against the tool-call log. It is evaluated on a 117-task function track (from the Agents4PLC benchmark, with 43/117 tasks audited and repaired) and a 65-task project-context track (from Spec2Control, across ten industrial plants) against baselines LLM4PLC, AutoPLC, and Agents4PLC, run on seven backbone models (MiniMax-M2.7, MiniMax-M3, Qwen3.5-Plus, DeepSeek-V4-Flash, DeepSeek-V4-Pro, GLM-5.2, GPT-5.5).

Key results:

• Function track: highest strict verified pass rate on all 7 models, mean 72.6% (vs. 63.9% for the strongest baseline Agents4PLC), including 82.1% vs 79.5% on GPT-5.5; bare-vs-full harness ablation shows +17.3 points on average (55.3% bare → 72.6% full).
• Project-context track: best mean integrated compilation (89.4% vs 58.7–81.5% for baselines), best mean static behavior (81.6 vs 71.7–75.7), and best mean dynamic behavior (52.2 vs at most 31.4 for AutoPLC), best on all 7 models with dynamic score never falling below 30 (baselines drop to single digits on worst models).
• Layer ablation (DeepSeek-V4-Flash): dynamic score rises monotonically from 23.1 (generation only) → 30.3 (+spec) → 43.7 (+compile) → 54.1 (+runtime), while static behavior moves far less (71.5 → 78.0); cost rises from 34k tokens/8.9 requests per task to 129k tokens/47.8 requests.
• Interaction cost: function track wall-clock 71s vs 454s for Agents4PLC (comparable request counts, 6.5 vs 6.3/task); project track requests 34.1 vs 6.9 per task at comparable wall-clock (347s vs 344s).
• Formal-verification coverage gap: 0.0% of 174 properties across 32 timer-bearing programs reached a conclusive (satisfied/violated) verdict from the formal pipeline (PLCverif/nuXmv), motivating runtime validation.

Why it matters / caveats: Methods with similar static/compilation scores can differ sharply in runtime behavior (baselines span only 22.4–31.4 dynamic points vs SEMAPLC's 52.2), showing that benchmarks stopping before execution cannot distinguish reliable from unreliable PLC code generators. The authors note two limitations: dynamic scoring is confined to a bounded scenario set derived from the hidden reference (behavior under unseen conditions is unmeasured), and SEMAPLC's advantage narrows on the strongest model (e.g., only a 1.8-point dynamic lead over Agents4PLC on GPT-5.5, and it trails on static behavior there).

Problem: Existing video generation benchmarks assess visual fidelity, temporal coherence, and prompt adherence but fail to jointly evaluate whether a generated video achieves an instructed outcome while preserving task-relevant semantic grounding with a reference image.

Method: The authors formulate "Semantic Task Completion Video Generation" and build SemComp-Data via a four-stage curation pipeline (Candidate Filtering, State Mining, Video Extension, Instruction Structuring) applied to ~20K videos sampled from the Koala-36M dataset, yielding 1,273 image-text-video triplets across six domains and 21 categories, each with a reference frame, paired brief/detailed instructions, and an outcome-centric video clip (avg. 4.03s). SemComp-Bench then evaluates generated videos using the VLM Doubao-Seed-1.8 (via Volcano Engine API) to answer structured binary questions over 27 uniformly sampled frames, producing an Outcome Achievement (OA) Score (conjunctive pass rate across outcome realization, semantic grounding, grounded entity consistency, and global visual continuity) and a Generation Reliability (GR) Score (mean pass rate across physical plausibility, visual clarity, artifact-free rendering, within-scene spatiotemporal coherence, and text/interface integrity), each averaged over three independent VLM evaluation runs.

Key results:

• On SemComp-Core (60 domain-balanced instances) with detailed instructions, HunyuanVideo-1.5-720P-I2V achieved the best OA Score at 37.8%, followed by Wan2.2-I2V-A14B at 28.3%; Phantom-1.3B was lowest at 3.9%.
• Seedance 2.0 achieved the best GR Score at 91.8%; among open-source models Wan2.2-I2V-A14B was best at 89.0%; within-scene spatiotemporal coherence was the weakest criterion across all models (pass rates 0.328–0.739).
• I2V conditioning consistently outperformed T2V under detailed instructions (e.g., Wan2.2-A14B: 28.3% I2V vs. 4.4% T2V OA Score; HunyuanVideo-1.5: 37.8% I2V vs. 4.4% T2V).
• Brief instructions caused severe degradation in T2V settings (e.g., HunyuanVideo-1.5 T2V dropped from 4.4% with detailed instructions to 1.7% with brief instructions).

Why it matters / caveats: The results show that even the best current video generation models achieve intended outcomes with task-relevant semantic grounding in under 40% of cases, indicating this capability remains largely underexplored; the paper notes the potential of SemComp-Data for task-specific training "remains to be empirically validated," meaning it is currently proposed only as an evaluation resource, not a training solution.

Problem: Existing AI-scientist systems automate the research workflow (hypothesis generation, code execution, manuscript writing) end-to-end, but they only reason over text, code, labels, or precomputed summaries, leaving scientifically decisive spatial, temporal, cross-channel, and procedural relations in raw multimodal evidence unavailable to the agent.

Method: OmniScientist is an end-to-end, omni-modal, discipline-agnostic AI scientist built from a perception layer plus 3 autonomous agents (ideation, experiment, writeup), each running a ReAct loop, wrapped in a thin deterministic pipeline that controls stage transitions and re-ideates on collapsed experiments or null results. Scientific evidence is organized into 4 discipline-independent families (perceptual, symbolic, quantitative-statistical, procedural) covering 12 modalities; the perception layer prioritizes native numeric analysis (e.g., FFT peaks, trend points) and invokes budget-constrained visual rendering only when spatial/structural patterns are essential. Code-enforced checks run at each stage: idea checks (novelty/falsifiability via OpenAlex/Crossref literature search), a rigour check / exit check (Algorithm 1) validating execution provenance, multiple-comparison correction, independence/leakage tests, and anti-HARKing rewriting, and a claim check that traces every reported number and claim back to the execution record before compiling the final PDF via one of 5 venue-style structural specifications.

Key results:

• Completes the full path from raw data to a compiled manuscript in all 36 of 36 demonstration cases spanning 5 discipline families and 4 evidence families.
• Achieves a mean overall paper score of 6.3 (0–10 scale, 7-dimension rubric, 2-judge cross-family panel) with the reference Claude Sonnet 5 reasoning backbone.
• In paired comparisons against a blind variant (precomputed scalar features only), the full-perception system wins 85% of head-to-head judgments and improves all 7 evaluation dimensions, with the largest gains in multimodal grounding (+2.8) and significance (+1.8).
• Leave-one-out ablation: removing prior-art search drops score from 6.9 to 5.7 (steepest decline); removing the novelty check causes a full-point drop in the novelty dimension specifically.
• Across 9 reasoning backbones (Sonnet 5, GPT-5.6, GLM-5.2, Kimi K2.7, Qwen3.5 9B/27B/122B, Gemma-4 26B/31B), completion rates and mean composite scores range from 36/36 cases at 6.5 (Sonnet 5) down to 18/32 cases at 4.1 (Qwen3.5-9B); a full 3-stage run costs $0.03–$4.34 depending on backbone.
• Case-cited numeric findings from raw perception include: 21.7% (163/750) of "noise"-labelled seismic traces actually carry coherent transients; galaxy morphology reading holds at 83.8% vs 81.0% (McNemar p=0.63) across survey depths; leave-one-family-out RMSE is 3.1×–7.0× higher than random k-fold CV in materials informatics.

Why it matters / caveats: The results argue that lifecycle-wide direct perception of raw evidence (not just precomputed features) is essential for grounding scientific hypotheses and claims, and that a single domain-agnostic engine can generalize across 16+ disciplines by swapping only a specification file. The evaluation relies on LLM judges (deepseek-v4-flash, gemini-2.5-flash-lite) for paper quality scoring, and weaker open-weight backbones (e.g., Qwen3.5-9B, Gemma-4-26B) show substantially lower completion rates and scores, indicating the system's quality is still bottlenecked by the reasoning backbone's capability.

Problem: Self-rewarding RL removes the need for costly ground-truth labels by deriving reward from a model's own completions, but because the reward signal comes from the same policy being optimized, it can amplify existing biases, reduce response diversity, and lead to training collapse.

Method: CO-RL trains a cohort of N independently parameterized policies (sharing no parameters or gradients) that supervise each other: each agent samples K completions per unlabeled prompt, aggregates them via majority vote, and each agent's rollouts are rewarded (binary, 1/0) against the majority-vote pseudo-label of a designated peer (a directed ring for N>2), rather than its own vote. Each policy is updated independently with GRPO (or REINFORCE++) using these cross-agent rewards. Diversity is maximized across three axes — decoupled optimization, heterogeneous model families/sizes (e.g., Qwen2.5 paired with Llama-3, or Qwen3-1.7B/InternVL/Gemma-3), and diversified input formation (one agent trained on original MATH prompts, the other on DeepSeek-V3 rewrites) — to reduce correlated errors between peers. Training uses AdamW, temperature 1.0, LR 3e-6 (LLMs) / 1e-6 (VLMs), K=12 (LLMs) / K=8 (VLMs) rollouts, on MATH (level 3–5) for text and MMR1-Math / multimodal-open-r1 for vision-language models, on 8×H100 GPUs.

Key results:

• CO-RL yields average gains of 3.0–8.6% across seven text-only benchmarks for LLMs and 2.3–7.2% across four multimodal benchmarks for VLMs (2B–12B parameter range).
• Outperforms the strongest self-rewarding baselines (TTRL, RENT, Intuitor, Co-rewarding-II) by 0.8–2.0% on average across text benchmarks.
• Under the CoMAS controlled evaluation setting, CO-RL outperforms prior multi-agent RL methods by 4.0% on average while using only half as many agents (and no LLM judge or learned reward model).
• Three-agent CO-RL (Qwen2.5-3B, Llama-3.2-3B-Instruct, Qwen3-1.7B trained jointly) improves the three base models by average gains of 7.8%, 6.0%, and 8.2% respectively, matching or outperforming GT-Reward (ground-truth supervised GRPO) for all three models.
• Same-family CO-RL alone (two agents from the same base model) already improves Qwen2.5-3B and Llama-3.2-3B-Instruct average performance by 8.0% and 4.0% respectively over base.

Why it matters / caveats: Cross-agent supervision provides an independent, decorrelated learning signal that avoids the self-reinforcing feedback loops of single-model self-rewarding, and the paper backs this with a theoretical analysis (Proposition 1–2, Theorem 1) showing self-rewarding is "self-confirming" while CO-RL enlarges the basin of correct convergence given complementary agent errors. The framework is lightweight (no external judge/reward model, only reward-stage interaction) and generalizes across text and multimodal domains without ground-truth labels, in several settings matching or surpassing supervised GT-Reward training; the authors note future work is needed on how agent count, diversity, and interaction topology affect the framework, and adaptive supervision mechanisms.

Problem: Existing training environment pools for language agents (hand-curated, statically synthesized, or built with a frozen generator) keep the goal distribution fixed as the learner improves, so agents stop improving once they exhaust them.

Method: SPADE has a single LLM play two roles with shared parameters: an Environment Designer that writes complete Gym-style (reset()/step()) executable Python environments (full MDPs with state transitions, reward functions, and verification code) plus a privileged hint, and a Reasoning Agent that solves them with and without the hint; both roles are trained jointly via GRPO. The Environment Designer's reward is a hint-based regret signal, rD(e) = r̄A(e|h) − r̄A(e) (the Reasoning Agent's average return with vs. without the hint), blended with a flat-top difficulty anchor (win-rate band [0.4,0.6]), and is stabilized with per-role advantage normalization, a delayed/off-policy-corrected designer update (truncated importance sampling), and asymmetric clipping. The Designer is grounded on documents sampled from a large pretraining corpus (DCLM/MegaScience for games, Nemotron code corpus for tool use) and an accumulated environment memory of past high/low-regret environments, and is trained/evaluated at up to 30B-A3B scale on Qwen3 backbones (4B, 8B, 30B-A3B).

Key results:

• Games setting (30B-A3B): SPADE reaches a suite average of 58.3 across eight held-out benchmarks (AIME'25/26, GPQA-Diamond, LiveCodeBench-v6, 4 Reasoning-Gym categories), +8.1 over the untrained base and +5.3 over the strongest fixed-environment baseline (Fixed-env RLVE), with up to +7.5 on individual benchmarks.
• Tool-use setting: SPADE improves BFCL v4 multi-turn by +10.3 at 4B and +5.7 at 30B-A3B, and ACEBench-Agent by +13.9 at 30B-A3B (30B-A3B avg across BFCL v4 MT/τ2-bench/ACEBench-Agent rises from 49.0 to 61.1, +7.7).
• Ablations: freezing the Environment Designer and dropping its memory drops the suite average to 40.5 (9.7 points below untrained base of 50.2); a fixed frontier-model (GPT-5.5) designer with corpus grounding and memory reaches 53.0, recovering only about 35% of SPADE's +8.1 gain.
• Corpus grounding sustains environment diversity: Vendi/n = 0.68 with the corpus vs. 0.04 without; the no-corpus ablation repeats the same RotatingMazeEnv task 41 consecutive times over steps 290–312.
• Trained Environment Designer environments become harder over training: the share of Physics environments revealing the governing formula in the opening prompt falls from 25% to 5% over 473 environments, and reward granularity increases from 3.7 to 5.8 distinct levels per environment.

Why it matters / caveats: By making environment design itself a learnable, co-evolving RL component rather than a fixed generator or hand-curated pool, SPADE offers a route to open-ended self-improvement in LLM agents without external environment supply; results are validated up to 30B-A3B scale in two domains (games and tool use), though the paper notes expected regret can dip below zero for smaller backbones when hints mislead the policy, and each ablation control changes more than one factor at once (e.g., freezing the designer also removes its memory), limiting precise attribution of gains to any single component.

Problem: Single-step retrosynthesis (SSRS) is inherently one-to-many (a target molecule can be made via many valid disconnections), but standard single-answer evaluation/benchmarking and training protocols fail to capture this diversity, and prior work showed LLMs as SSRS models still lag behind the best conventional (non-LLM) models on chemical plausibility.

Method: The authors introduce Top-K prompting/training (appending "Give me 15 different answers" to prompts, generating K=15 candidate reactant sets per target) as an alternative to standard Top-1 prompting, and compile CREED-CCV-2+USPTO-XL, a ~45.6M-reaction dataset (3,680,906 unique products) built by merging CREED-CCV-2 (ChEMBL v34 compounds enumerated via a Virtual Synthesis Engine plus CREED-CCV, verified with ChemCensor v1.1.1) and USPTO-XL (USPTO products with VSE-enumerated reactants, ChemCensor-verified). They train C3LM (Chemistry Constraint-Consistent Language Model), initialized from the LFM2 2.6B checkpoint, via supervised fine-tuning (SFT) in Top-K mode on this dataset, followed by reinforcement fine-tuning (RFT) using single-reward GRPO (group size 8, temperature 1, KL weight 0.1, 1000 steps, learning rate 10⁻⁶, 64 groups/step) with a weighted 6-component reward: thinking-format validity (0.1), molecular syntax/valid SMILES (0.5), ChemCensor plausibility score (1.0), Top-K uniqueness (0.2), Top-K count matching (0.1), and a novelty score (1.0) rewarding plausible reactants absent from the training set's exhaustive reactant list.

Key results:

• Switching C3LM-LFM2-CREED-CCV+USPTO training from Top-1 to Top-K mode improved Max/@3/@5/@10 Av. PT-Top-K CC metrics on URSA-expert-2026 by +0.30/+0.62/+0.70/+0.60 respectively (Av. PT-Top-10 improved >2.5-fold).
• Scaling the training set >6x (CREED-CCV+USPTO → CREED-CCV-2+USPTO-XL, ~45.6M reactions) further improved Max/@3/@5/@10 by +0.12/+0.13/+0.17/+0.29.
• RFT with the ChemCensor (CC) reward added +0.04/+0.07/+0.06/+0.02 to Max/@3/@5/@10, and adding the novelty reward (NR) contributed a further +0.08/+0.06/+0.08/+0.08, yielding the final C3LM-LFM2-RFT-CC-NR model with Max=2.16, Av. PT-Top-3=1.94, Top-5=1.73, Top-10=1.37 on URSA-expert-2026 — outperforming all conventional SSRS models and open-weight LLMs on this metric, though best proprietary GP LLMs (Gemini 3.1 Pro, GPT 5.5, Max=1.94/1.91) remain close on Max CC.
• C3LM-LFM2-CREED-CCV-2+USPTO-XL and C3LM-LFM2-RFT-CC-NR generated more unique plausible reactions on average than MHNreact (the most diverse conventional model), by +0.4 and +0.3 reactions per target respectively, while other LLMs trailed MHNreact by -2.7 to -9.4 reactions per target.
• Aggregating all 30 benchmarked models revealed a frontier still +0.44 above best-model Av. PT-Top-10 and +0.31 above best-model Av. PT-Top-5, indicating unrealized headroom in plausibility/diversity.

Why it matters / caveats: The work establishes Top-K prompting/training as a practical best practice for both LLM benchmarking and training in retrosynthesis, and shows LLMs and conventional models explore largely complementary reaction spaces, motivating ensemble CASP systems. The authors acknowledge partial circularity between optimizing on ChemCensor rewards and evaluating with ChemCensor metrics, and note limitations including ChemCensor's inability to model reaction conditions/solvents/purification, its reliance on patent-derived reference data, exact-SMILES-based diversity assessment, and potential bias from the template-based data-generation engine toward known chemical patterns.

Problem: In JEPA-style latent world models used for MPC planning, strong decoding of task variables from the latent (information sufficiency) does not guarantee that the Euclidean-distance planning cost correctly ranks candidate action sequences by real task progress (decision-metric alignment), and this gap is not measured by standard probes.

Method: The paper introduces two diagnostics — Plan-Real Spearman (rank correlation between latent Euclidean cost and environment-rollout cost over N=64 random CEM-proposal candidates across n=30 held-out start/goal pairs) and CEM-stage Spearman (the same correlation measured at random, mid, and elite CEM iterations) — plus a sufficient-condition analysis (Proposition 1, Corollary 1) identifying encoder distortion (Lf/µf), terminal rollout error (ϵH), and candidate margins as the controlling quantities under bi-Lipschitz and rollout-consistency assumptions. Building on LeWM (a ViT-Tiny-encoder, Transformer-predictor JEPA world model trained with SIGReg), they propose DA-LeWM, which adds two lightweight two-layer MLP auxiliary heads — an inverse-dynamics head (Linv) and a demonstration-conditioned goal-action head (Lgoal) — with loss LDA-LeWM = LWM + α·Linv + β·Lgoal (α=β=0.1), trained/evaluated via CEM-based MPC on PushT, Reacher, Cube, and TwoRoom.

Key results:

• On PushT, Plan-Real Spearman rises from +0.280 (LeWM) to +0.420 (inverse-only) and +0.412 (DA-LeWM), while linear-probe R² scores vary by at most 0.03.
• PushT online success (matched one-epoch budget): LeWM 49.3% ± 12.2 → inverse-only 64.0% ± 7.2 → DA-LeWM 92.7% ± 1.2 (a 43.4 pp gain over LeWM); Cube improves from 62.7% to 73.3% (+10.6 pp).
• Removing SIGReg collapses the latent cost-surface ratio from 3–30× to ≈1.005×, driving Plan-Real Spearman to ≈0 and online success down to 2.0% (PushT), 41.3% (TwoRoom), 10.7% (Reacher).
• CEM-stage Spearman for DA-LeWM: +0.536 (random) → +0.253 (mid) → −0.011 (elite), versus LeWM's +0.403 → +0.227 → +0.036 — showing gains concentrate at random/mid stages while elite-stage correlation saturates near zero for all variants.
• After 10-epoch training, DA-LeWM beats LeWM by 2.7 pp (PushT, 98.7%), 1.3 pp (Reacher, 87.3%), 6.7 pp (Cube, 80.7%), and 9.0 pp (TwoRoom, 96.0%), and is competitive with/exceeds PLDM and DINO-WM baselines on most tasks.
• Latent-displacement-vs-action-magnitude rank correlation rises from −0.03 (LeWM) to +0.38 (inverse-only) and +0.43 (DA-LeWM), measured over 38,400 held-out transitions.

Why it matters / caveats: The work shows that linear probes alone are insufficient to evaluate latent world models for control — two representations with near-identical decodability can differ by 43 percentage points in online MPC success — motivating decision-metric-aligned diagnostics and cheap action-conditioned auxiliary losses as a practical fix. Limitations acknowledged by the authors: evidence is restricted to four short-horizon simulated tasks with a single ViT-Tiny/LeWM-family checkpoint per configuration and one training run each (no initialization-variance estimate), Euclidean costs and CEM only (no gradient-based planning), α fixed without sensitivity sweep, and generalization to real robots, partial observability, larger backbones, other planners, or learned reward/value-aware costs remains untested.

Problem: Given two compatible open-weight language model checkpoints, determine from weights alone (no data, activations, or forward passes) whether they share weight-level ancestry, as opposed to merely behaving similarly.

Method: For each residual block, the authors compute the branch product Mℓ = WK...W1 (composing the linear factors of the residual branch, dropping nonlinearities), decompose it into an identity-aligned component (measured by normalized trace concentration s(M)=|tr(M)|/‖M‖F) and a traceless "centered" remainder Eℓ, and normalize the remainder into a checkpoint-specific unit-vector signature ϕℓ = vec(Eℓ)/‖vec(Eℓ)‖. Corresponding blocks between a reference and suspect checkpoint are matched via Hungarian algorithm assignment on a signature-similarity matrix, and a symmetric lineage score L(A,B) averages cosine similarity of matched signatures across blocks, calibrated against an empirical null built from independently trained models (RELATED if L(A,B) exceeds the max null score). Evaluated against baselines including weight cosine similarity, aligned Frobenius distance, singular value distance, SVCCA, CKA, IPGuard, and Re-Basin+scale, on a residual-MLP benchmark (depth-16/24 MLPs) and a ~30M-parameter GPT-2 benchmark trained on TinyStories, plus a public LLaMA-2 checkpoint case study.

Key results:

• AUROC=1.0 on both MLP (52 pairs) and GPT-2 (45 pairs) benchmarks, separating fine-tuned, LoRA-merged, pruned, and quantized descendants from independent and distilled models.
• Under function-preserving checkpoint laundering (permutation, rescaling), the method stays at AUROC=1.0 while weight cosine degrades to 0.80 and aligned Frobenius drops to 0.50 (permutation) or 0.0 (strong rescaling) on MLP; on public LLaMA-2/LLaMA-3/Mistral/Qwen2.5 derivatives, the lineage score changes by ∆L < 10⁻⁷ versus weight cosine losing ~95% of its signal.
• Runs 76× faster than the nearest robust baseline (Re-Basin+scale) on GPT-2 (5.1ms vs 387.9ms) and 283× faster than SVD.
• Projection-pair (block) recovery via Hungarian matching reaches 100% accuracy on the canonical MLP path across six language model families (GPT-2, BERT, LLaMA-2, Mistral, Qwen2.5, DeepSeek-R1), versus ≤4% for random-init baselines.
• LLaMA-2 case study: 3 documented descendants scored L = 0.995, 0.996, 0.336, while all 7 independent architectural clones (OpenLLaMA, Amber, Baichuan, InternLM, Yi) scored |L| < 5×10⁻⁵.
• Centering reduces spurious similarity between independent GPT-2 models from 0.027 to 0.0015 (an 18× improvement) compared to uncentered branch-product cosine similarity.

Why it matters / caveats: The method offers a passive, data-free provenance signal for auditing open-weight model supply chains without requiring proactive watermarking or trusted metadata, and it algebraically distinguishes weight ancestry from mere behavioral mimicry (e.g., distilled students). Limitations: it requires white-box access to both checkpoints with matching depth/hidden dimension and residual architecture (no cross-architecture or API-only verification), the score is symmetric so it cannot establish direction of descent, it degrades under aggressive transformations (e.g., L=0.58 at 85% pruning sparsity, L=0.336 for CodeLlama's extensive continued pretraining), and it is vulnerable in principle to orthogonal rotation laundering on architectures using unparameterized normalization (though the authors are not aware of widely deployed models in that category).

Problem: It is unknown whether looped (recurrent-depth) language models, which have shown gains on reasoning benchmarks, actually improve agentic tool use, particularly compositional tool calling where models must coordinate multiple API calls, maintain intermediate state, and preserve dependencies across tool interactions.

Method: The authors evaluate native looped models (Ouro-1.4B, Ouro-2.6B, which apply a shared stack of Transformer layers for up to Tmax=4 recurrent iterations with a learned per-token halting gate) against standard Transformer baselines (Qwen3, Llama), and also evaluate retrofitted looped models (Llama-3.2-1B and OLMo-2-1B converted via the McLeish et al. 2025 recurrence retrofit with a prelude/shared recurrent block/coda structure, trained with a Poisson-lognormal sampled recurrence depth and KL regularization to a frozen base model) against their non-looped parents. All controlled models are supervised fine-tuned on the Hermes Function-Calling V1 dataset (ChatML/Hermes JSON protocol) with matched LoRA (rank 32) and optimization settings (AdamW, lr 2e-5, 2 epochs, bf16, on an A100 80GB), and evaluated on BFCL v3 (AST accuracy), NESTful (Win Rate and F1 metrics), and API-Bank (Call Correctness, Exact, Parse), using both fixed-depth and adaptive (confidence-threshold-based) inference.

Key results:

• On BFCL, Ouro-2.6B SFT reaches 86.4% overall AST accuracy vs. 56.7% for Qwen3-4B SFT under matched fine-tuning, with the largest gaps on Parallel (81.0% vs. 5.5%) and Parallel-Multiple (60.5% vs. 6.0%) categories.
• Retrofitted Llama-3.2-1B looped SFT reaches 32.9% overall BFCL accuracy vs. 21.4% for the non-looped Llama-3.2-1B SFT baseline (at fixed depth 8).
• On NESTful, Ouro-2.6B SFT achieves Win Rate 0.204 vs. 0.191 for Ouro-2.6B Base, and Ouro-1.4B/2.6B substantially outperform Qwen3-1.7B/4B Base (Win Rate 0.000) and Qwen3 SFT (0.063/0.134).
• On API-Bank, differences are much smaller/model-dependent: Ouro-2.6B SFT gets 79.9% Call Correctness vs. Qwen3-4B SFT's 68.5%, but retrofitted OLMo-2-1B Looped SFT (34.0%) is actually slightly below its non-looped SFT counterpart (37.1%).
• Increasing fixed recurrent depth generally raises accuracy on compositional BFCL/NESTful categories before plateauing (e.g., Ouro-2.6B nears saturation after 3 iterations); adaptive inference (via Ouro's exit gate, thresholds q∈{0.1,0.3,0.5,0.7,0.8}) matches or exceeds the best fixed-depth accuracy while using fewer average loop iterations per token.

Why it matters / caveats: The results suggest looped architectures are a promising foundation for agentic systems needing reliable multi-step planning and dependency-aware tool coordination, with adaptive computation offering a better compute-performance trade-off than fixed depth. Caveats: no non-looped Ouro counterpart trained under identical pretraining exists, so comparisons to Qwen3/Llama are only approximate; retrofitted models remain substantially weaker than natively recurrent models on deeply nested NESTful workflows; and all evaluations are static, single-turn benchmarks, leaving live/multi-turn settings for future work.

Problem: Existing manipulation benchmarks score deformable-object tasks by task success alone, which cannot distinguish a policy that handles an object gently from one that completes the task while slipping or over-compressing it, because no prior dataset pairs policy-visible contact observations with independent, evaluator-only physical ground truth over complete tasks.

Method: SoftVTBench is a visuo-tactile dataset built in Isaac Sim/Isaac Lab containing 4,000 expert demonstrations across 40 pick-and-place tasks (four suites: Object-Soft, Spatial-Soft, Object-Rigid, Spatial-Rigid) and 50+ assets, including volumetric deformable objects rendered with FEM meshes and visually matched rigid twins; each 20 Hz episode synchronizes multi-view RGB, dual-finger tactile RGB and marker motion (via TacEx/Taxim/FOTS simulating GelSight Mini sensors), proprioception, language, binary/continuous gripper actions, and an evaluator-only stream of FEM nodal states hidden from the policy. On top of this, a closed-loop benchmark computes a per-object calibrated deformation tolerance (τo, via scripted grasp/lift/hold probing) and defines the Deformation-aware Success Rate (DSR), which credits an episode only if it both reaches the task-success predicate and keeps peak normalized deformation (Rmax) ≤ 1; this is compared against plain Task Success Rate (TSR) using Diffusion Policy, π0.5, and FastWAM under paired vision-only (VO) vs. visuo-tactile (VT) and binary (B) vs. continuous (C) gripper-control settings, evaluated under matched ID and OOD (lighting/mass/stiffness single-factor shift) protocols.

Key results:

• All 12 in-distribution deformable-object policy configurations contain successful rollouts that violate the deformation tolerance, accounting for 0.7–24% of each configuration's successes.
• Diffusion Policy VT-C shows a TSR−DSR gap of 9.6 percentage points on Object-Soft and 8.0 points on Spatial-Soft (24% of that configuration's successes), an exact count of 48 and 40 episodes respectively out of 500.
• Hidden violation rate (share of successes outside the safety zone) spans 10–24% for Diffusion Policy, 8–20% for π0.5, but only 0.7–6.5% for FastWAM; on spatial suites FastWAM keeps TSR and DSR within 0.4 percentage points.
• Sensing-vs-control ablation on π0.5 (Object-Soft): continuous control alone (VO-C) raises TSR by 11.4 points over VO-B, tactile alone (VT-B) raises it by 10.8 points, and combining both (VT-C) reaches 41.4%—no further gain over VO-C alone; VO-C is 10.4 points higher in DSR than VT-B (38.4% vs. 28.0%).
• Under OOD shift (pooled over 9 conditions), visuo-tactile (VT-C) beats vision-only (VO-C) in TSR in all 6 policy–suite comparisons (sign test p=0.016) and in DSR in 5 of 6, whereas in-distribution the TSR comparison is split 4-of-6.
• Across 2,000 deformable-asset demonstrations, tolerance-normalized peak deformation Rmax has a median of 0.433 and 95th percentile of 0.713, and no demonstration exceeds its tolerance.

Why it matters / caveats: The benchmark shows that touch availability alone does not guarantee effective multimodal fusion or safer interaction—tactile gains can be confounded by control granularity, and adding touch sometimes widens the TSR−DSR gap (e.g., Diffusion Policy's gap grows from 2.6 to 6.8 points under shift for π0.5)—while FastWAM demonstrates that high completion and safe interaction can coexist, arguing the success–safety gap is a tractable policy/control problem rather than an intrinsic cost of deformable-object handling.

Problem: Existing agent benchmarks measure either long-horizon execution or multi-agent competition in isolation, so it remains unmeasured whether LLM agents can sustain effective decision-making over long horizons with cumulative consequences while competing against other adaptive agents for the same resources.

Method: FM-Bench has an LLM agent manage a football club for 20 in-game years (roughly 340–400 decision stops) through a 26-tool schema-generated interface, drafting a squad under a fixed shared budget, trading players, negotiating contracts, investing in facilities/youth, setting lineups, and answering to a board that can fire it; a deterministic engine (no LLM judge or human rater) computes a single composite score (Sfinal) from three log-compressed channels — cumulative honors (H), net-worth value added (VA), and squad value (M) — with an early-settlement discount ρ. The environment instantiates four "management" demands (hidden information via biased scouting, cumulative/delayed-payoff consequences, a counter-adaptive transfer market, and multi-objective board pressure), and the agent's only cross-stop memory is a self-authored notebook, since every decision stop is a fresh conversation. Evaluation runs in two modes: a Solo track (15 frontier models vs. a frozen scripted world with scripted anchors: oracle, heuristic, idle, random) and an Arena track (the same 15 models plus a scripted heuristic anchor competing in one shared 20-year world), each replicated across three seeds (solo) or one seed-7 world (Arena).

Key results:

• claude-fable-5 tops the Solo track with Sfinal = 90.94 ± 5.20, reaching ~95% of the privileged oracle's 95.54 ± 4.68 (which reads true hidden state through the same interface).
• All 15 tested LLMs complete every 20-year horizon across seeds, while blind scripted baselines die out in 7 of 9 runs (heuristic 17.05 ± 12.34, idle −0.90 ± 1.86, random −17.21 ± 2.45).
• Six behavioral capabilities correlate with score: endgame awareness (Spearman rs = −0.58), credit assignment / idle cash (rs = −0.50), and proactive contract renewal (rs = +0.45) track score on every seed, while token spend is uncorrelated (rs = −0.19, all p > 0.38, n = 15) despite a 7x range (28M to 194M tokens).
• Median offers needed per completed transfer signing is 30 (field median), vs. 1.0 for the oracle and 9 for the best model (claude-fable-5), showing no model learns the market's hidden prices even after hundreds of rejected bids.
• In the Arena, claude-fable-5 wins overall (Sfinal = 76.26) but the league title rotates among 10 different models across 19 season transitions (reigning champion retains title in only 2 of 19 transitions), unlike the Solo track where the top 4 models hold the title continuously.
• Of six first-play humans, four died out (all below the disciplined heuristic anchor) and the best human score (74.64) lands only at the bottom of the model board, 16 points behind claude-fable-5.

Why it matters / caveats: The results show that competitive, adaptive multi-agent long-horizon settings reorder model rankings that appear stable in solo play (e.g., mid-game leaders like gemini-3-flash collapse by year 20), and that scale, price, and vendor do not predict performance — highlighting managerial behavior over raw compute as the differentiator. Caveats acknowledged by the authors: the Solo board rests on only three seeds and the Arena is a single seed-7 world with no error bars, so close orderings should be read as ties, and the 15 scripted Solo opponents are disciplined but non-adaptive by design.

Problem: Public creative writing datasets for LLMs remain heavily story-centric, limiting models' ability to follow the distinct structural, stylistic, and formatting conventions of non-story creative genres (e.g., rap, screenplays, game design documents).

Method: The paper proposes attribute-guided genre expansion, a three-stage synthetic data pipeline that separates thematic variation from genre-form control: (1) thematic seed sampling draws n=5 few-shot query–response pairs from a filtered r/WritingPrompts pool for topical diversity, (2) genre attribute sampling selects a random subset A (with k ~ Uniform(0, |A_g|)) from a manually curated, Wikipedia/writing-manual-derived list of 5–15 structural attributes per genre, and (3) attribute-guided pair synthesis combines these via a meta-prompt template into GPT-5-mini to generate 5 queries per instantiation (using verbalized sampling for diversity), with responses generated by Qwen3-235B-A22B-Thinking and quality-filtered via an independent Qwen3-30B-A3B-Instruct LLM-judge (excluding scores below 2 standard deviations from the mean). This produces the Multi-Genre Collection, a 50K-instance corpus across 13 genres, used to fine-tune Llama-3.1-8B-Instruct, EXAONE-3.5-7.8B-Instruct, and Qwen3-8B via LoRA (rank 128, all transformer layers) SFT with LlamaFactory.

Key results:

• On the held-out Multi-Genre test set, SFT improves Qwen3-8B from 67.7 to 69.3 (+1.6), EXAONE from 64.9 to 66.2 (+1.3), and Llama from 44.4 to 68.4 (+24.0)
• On Arena Hard (Creative Writing), Llama+SFT reaches 33.0 (+30.1 over base 2.9); Qwen3-8B+SFT reaches 34.2 (+26.2 over base 8.0)
• With 2K-sample fair comparison, Qwen3-8B fine-tuned on Multi-Genre Collection scores 7.5/59.1/71.4 (Arena Hard/WritingBench/Multi-Genre) vs. 4.9/55.5/65.9 for DeepWriting-20k and 7.0/56.4/69.6 for LongWriter-6k
• NoveltyBench Distinct novelty score rises monotonically with genre count: 3.87 (0 genres) → 3.93 (4) → 4.26 (8) → 4.56 (12) → 4.81 (all 13)
• Under independent judges on WritingBench, Qwen3-8B+SFT scores 71.5 (GPT-5-mini), 65.9 (DeepSeek v3.2), 63.6 (Gemini 3 Flash), consistently above base Qwen3-8B (58.5/51.5/56.1); human evaluation gives Multi-Genre 59.3 vs. LongWriter 57.7 and DeepWriting 56.9

Why it matters / caveats: The framework demonstrates that genre diversity, not just thematic diversity or dataset scale, is a key driver of robust creative writing capability, and that fine-tuned models beat both base models and a writing-specialized baseline (LongWriter-glm4-9B) as well as models trained on prior writing corpora; the evaluation relies substantially on LLM-as-judge scoring (cross-checked with human evaluation on only 50 WritingBench prompts), and all queries/responses in the dataset are synthetically generated by GPT-5-mini and Qwen3 models rather than sourced from real users.

Problem: Existing LLM unlearning methods apply uniform gradient pressure to every forget-set fact regardless of how popular (frequently seen during pretraining) it was, causing popular facts to be under-erased and recoverable while rare facts are over-erased, damaging retain quality.

Method: AdaPop assigns each forget-set fact a per-fact power-law exponent βi = a·si^(-b) computed from an external popularity proxy (e.g., Wikidata sitelink counts, LLM-as-Judge scores, or corpus frequency via infini-gram over Pile-train), which reweights the per-token ascent signal wi,t = sg[pi,t(θ)^βi] applied to the forget-loss NLL, with (a,b) fixed via two anchor scores (rare ≈100, popular ≈3,000) so that popular facts stay in a "pressure-sustaining" regime (β=0.1) and rare facts in a "self-limiting" regime (β=1.5). This is paired with a dual-ascent controller that treats the retain-loss penalty α as a Lagrange multiplier, updated once per epoch via projected gradient ascent based on relative retain-loss drift δk, to automatically balance forgetting against retain-set preservation without per-dataset hyperparameter search. Experiments use LoRA fine-tuning (r=32, αLoRA=64) on Llama-3.1-8B-Instruct, Gemma-7B-it, and Qwen2.5-7B-Instruct, evaluated on the DUET and RWKU benchmarks against baselines GA, GD, NPO, and WGA (plus 11 more in appendices).

Key results:

• AdaPop leaks ∼5× less forgotten content than competing methods under paraphrased queries and ∼1.6× less under adversarial reformulations.
• Among stable (non-collapsed) methods, AdaPop achieves the lowest forget Cosine Similarity in all six model×benchmark cells and the lowest forget ROUGE-L in 5 of 6, with mean retain ROUGE-L ≥ 0.855 on all combinations.
• On RWKU adversarial attacks, AdaPop is lowest on all three models (e.g., Llama forget ROUGE-L 0.262 vs. WGA 0.396, NPO 0.657); on DUET paraphrase it is best on Qwen (0.045) and Gemma (0.027), within seed noise of WGA on Llama.
• General capability preservation: NPO, WGA, and AdaPop all stay within 0.05 MMLU of the pre-unlearning checkpoint (Table 7).
• Ablation without the dual-ascent controller: retain falls to 0.769 vs. ≥0.927 with the controller, at matched rare-fact forgetting (Appendix N).
• Popularity-proxy robustness: Wikidata score and LLM judge disagree on 16% of labels yet differ by at most 0.03 in retention; even inverting every popularity label still holds retain at 0.92 with forget ≤0.05 due to exponent clipping.
• Corpus-frequency proxy (infini-gram over 383B-token Pile-train) correlates with Wikidata score at Pearson 0.970, Spearman 0.839 (in log space); LLM-judge correlation is weaker (Pearson 0.677, Spearman 0.659).

Why it matters / caveats: AdaPop is presented as the first training-time unlearning method to condition gradient updates on an external (model-independent) popularity signal rather than the model's own confidence, and internal metrics (∆Rank, Hid.Cos) show it disrupts the answer's parametric representation rather than merely suppressing surface output (unlike WGA, whose forget ∆Rank on DUET is negative — the gold token's rank actually improves post-unlearning). Limitations include dependency on an external popularity proxy that is unavailable for procedural/creative knowledge, exponent coefficients calibrated specifically to the DUET score distribution (requiring rescaling/recalibration for other proxy scales), and evaluation restricted to LoRA fine-tuning rather than full fine-tuning.

Problem: Existing token-level hallucination detectors score each token independently from a single signal, which fails on confidently-wrong hallucinations and discards the fact that hallucinated tokens form temporally extended spans rather than isolated point events.

Method: For each generated token, the authors extract a 33-dimensional feature vector fusing three signal families — 20 text-statistics features (context/n-gram overlap, novelty dynamics with running means, deltas, windowed extremes), 7 NLI features from DeBERTa-v3-large fine-tuned on Multi-Genre NLI (entailment/contradiction/neutral probabilities plus temporal derivatives), and 6 language-model surprisal features from TinyLlama-1.1B (log-probability, entropy, subword rank, fallback handling) — none requiring access to the generating model's internals. A 2-layer Bidirectional GRU (h=64, 121K params) with an MLP head is trained with class-weighted binary cross-entropy (Eq. 1) as a sequence labeler over this feature matrix, and compared against LogReg, MLP, 1D-CNN, BiLSTM, Transformer, and CRF-augmented variants on RAGTruth (with PsiloQA used for cross-dataset transfer).

Key results:

• BiGRU reaches 0.840 ± 0.007 AUC on RAGTruth (10 seeds), an 11.0-point gain over an independent LogReg baseline (0.730 ± 0.001), p = 0.002 (Wilcoxon signed-rank).
• Controlled decomposition attributes the 11-point gap to nonlinear capacity +3.5 (32%), sequence aggregation +2.6 (24%), and temporal order +4.9 (44%/45%).
• No single feature exceeds 0.641 AUC alone (NLI entailment drop); BiGRU with text-only features reaches 0.832 AUC vs. 0.845 AUC (single seed) with all signals.
• Cross-model leave-one-out generalization across six source LLMs yields mean AUC 0.808, a 3.8% relative degradation from in-distribution (0.840); GPT-4 is hardest (0.781), LLaMA-2-70B easiest (0.829).
• Cross-dataset transfer is asymmetric: PsiloQA→RAGTruth reaches 0.744 AUC vs. RAGTruth→PsiloQA at 0.634 AUC.
• CRF models scored with softmax are miscalibrated (e.g., BiGRU-CRF 0.666 AUC); forward-backward marginals recover up to +0.179 AUC (to 0.845).
• Combining the 33-dim feature set with Lookback Lens attention features (Qwen3-14B proxy) yields 0.866 AUC, versus 0.838 for Lookback alone and 0.819 for the black-box features alone.
• Information-theoretic analysis: persistence probability P(Ht|Ht-1)=0.902 vs. onset probability P(Ht|Ft-1)=0.006 (150:1 ratio); 76% of label entropy is resolved by one step of temporal context, 91% by both neighbors.
• Span-level F1 is 0.394, below LettuceDetect's 0.589 (395M-parameter fine-tuned encoder vs. this method's 121K parameters on pre-extracted features).

Why it matters / caveats: The method is black-box (no access to the generating LLM's internals), making it directly applicable to closed-source APIs and robust to unseen source models (under 4% AUC degradation), while an architecture-ceiling analysis (recurrent, state-space/Mamba, and attention models all converge to ~0.843–0.845 AUC) shows the bottleneck is the 33-dimensional feature set rather than model capacity. Limitations include lower span-level F1 versus fine-tuned encoders, added inference latency from running DeBERTa and TinyLlama at test time, English-only evaluation, and a binary formulation that does not distinguish hallucination severity or type.

Problem: Long-horizon agents that must choose which skill (instruction file) to read mid-episode from a large candidate slate cannot learn this selection ability from standard outcome-rewarded RL, because a structural failure called "selector credit starvation" starves the few skill-naming tokens of usable training signal.

Method: SkillGate partitions a trajectory's token support into two disjoint credit channels within one GRPO policy-gradient update: a task channel that applies the usual group-normalised outcome advantage to execution tokens only (with the entire skill-read tool call, wrapper and identity span, deleted from the mask), and a selector channel that applies an action-local, group-centred advantage to exactly the skill-identity tokens, positive only when a trajectory issues a single read and it is the oracle skill. Token weights are rescaled (Equation 2) so each channel's total loss mass stays constant regardless of trajectory length or number of credited actions, and the two objectives are combined with a selector coefficient λ=0.20 plus a KL penalty against a frozen reference (Equation 3). Training runs 100 steps of on-policy GRPO from a shared Qwen3.5-9B SFT checkpoint, 8 rollouts/prompt, batch 128, on 491 tasks under a standard 16-candidate mixed skill slate (1 oracle, 5 misleading hard negatives, 5 relevant + 5 irrelevant skills from a 2,045-skill public library).

Key results:

• SkillGate lifts trial success from 40.8% (SFT init) to 53.2% overall, versus 47.0% for the identical-budget outcome-only RL baseline (SkillRL).
• Cuts misleading-skill exposure by two thirds (from 55.0% down to 21.8%) while raising oracle-read exposure to 83.9%, and reads fewer skills (1.11 reads/trial vs. 1.88 for outcome-only RL).
• Audited on 12,800 training trajectories: skill-naming tokens carry a median 0.14% of trajectory loss weight (diluting ~7× from shortest to longest trajectories), nearly two in five oracle reads inherit a negative (wrong-signed) advantage, and correct skill choice is nonetheless worth +11.2 pp of task success within matched prompt groups.
• Outperforms supervised selection (Selection BC 46.2%, SelSkill-DPO 46.2% per table 46.0%/46.2%), Skill1 (44.7%), and surpasses reference models with roughly forty times the parameters (e.g., Qwen3.5-397B-A17B at 44.7% overall, DeepSeek-V3.2 at 43.6% oracle exposure); SkillGate is cheaper at inference too, cutting unique skill reads by 41.2% and turns by 5.2% versus outcome-only RL.

Why it matters / caveats: The work demonstrates that skill selection is a distinct trainable capability that doesn't emerge from scale or from outcome-only RL, and offers a general recipe (disjoint credit channels) for trajectories that interleave decisions of different kinds. Authors note limits: each configuration is a single run (uncertainty from task-level bootstrap, not seed replication), the method requires training tasks with known correct skills, and the action-local scheme cannot credit an abstention (choosing not to read).