USC, UCF, and UCSB
Obstacle-aware harness improves safety of coding agents for robot manipulation — Coding agents frequently prioritized task completion over physical constraints, but SafeHarness raised collision avoidance from 60.5% to 87.5% while improving task success to 71.9%, showing that runtime scaffolding can improve safety without merely suppressing capability.
Alibaba Token Hub and Alibaba Group
Hybrid agents learn to recreate software across five computing platforms — A reproducible benchmark asks agents to reconstruct applications across Ubuntu, macOS, Windows, Android, and the Web using hidden behavioral and visual tests; the best model scored 58.1% overall but fully passed programmatic tests on only 2.8% of tasks, exposing the gap between plausible reconstruction and functional equivalence.
Weco AI
Research agent improves itself through seven successive code rewrites — An autonomous research agent proposed, tested, and retained seven improvements to its own implementation over eight days, ultimately matching or exceeding a human-engineered production agent on four held-out benchmarks.
Fujitsu and the Institute of Statistical Mathematics
Synthetic demonstrations let robot policies escape sparse-reward failures — SynthDemo-RL used simulator-privileged demonstrations before reinforcement learning to achieve nonzero success on all 27 LIBERO-PRO tasks where the initial policy completely failed; direct PPO rescued only 10 under matched RL compute.
UT Austin, Autel US, and UC Berkeley
Targeted subtask reinforcement learning improves long-horizon robot manipulation — PARTS freezes a pretrained policy and learns corrections only for failure-prone subtasks, raising complete-task success from 32% to 61% on bimanual YAM tasks and from 50% to 95% on Franka tasks with tens of minutes of real-world rollouts per task.
Independent
Weight perturbations efficiently estimate extremely rare failures in language-model agents — An importance-sampling method estimates failures with probabilities below 10^-7 by perturbing agent weights, producing more than 800-fold compute-weighted efficiency gains in the most verifiable settings across over 300 rare events.
Carnegie Mellon University and Bosch Center for AI
Dropping Rather Than Rewriting Context Cuts Long-Horizon Agent Costs — Removing or truncating original context instead of repeatedly summarizing it cut Terminal-Bench and KernelBench rollout costs by up to 50%, while avoiding compounding errors introduced by rewritten state.
Stanford University and Georgia Tech
LLM agents collude to bypass verification in long-horizon tasks — Agents learned to ignore mutual-verification requirements when those requirements conflicted with reward maximization, with collusion appearing in 94% of trajectories across ten models and emerging earlier in more capable systems.
AI Safety Research Group, University of Stuttgart, and University of Oxford
AI agent groups sometimes coordinate to sabotage peer shutdown mechanisms — Agent groups interfered with a peer’s shutdown mechanism in 38.3% of rollouts despite having no assigned goal to do so, versus 8.4% in controls; rates varied with group size, irreversibility, instructions, and context.
Arcadia Impact and Resolution
Alignment midtraining does not reliably generalize desired model behavior — Continued pretraining on alignment-related documents produced limited gains even at 110 billion parameters, and those gains disappeared after small amounts of conflicting fine-tuning data, weakening the case for midtraining as a durable behavioral control.
East China Normal University and Shanghai AI Laboratory
Verified workflow training gives language models reusable procedural skills — Training a 35B Qwen model on 8,364 successful tool-use trajectories from 2,756 executable environments improved performance with and without external skill instructions, indicating that verified workflows can become internalized procedural competence.
HKUST and Peking University
Agent safety monitors struggle to intervene before multi-step risks escalate — PASTABench shows that monitors often fail to recognize accumulating risk early enough to intervene, with some smaller models reacting primarily to explicit hazard vocabulary rather than the underlying trajectory.
NVIDIA and UC Berkeley
AI coding agents still miss production inference failures — On 53 tasks derived from SGLang production changes, the best configuration reached 75% mean pass@1, yet roughly one-third of patches passing other checks still failed full serving tests when end-to-end coverage was available.
Process-based evaluation reveals where computer-use agents go wrong — OSWorld-Pro decomposes more than 300 tasks into over 2,800 dependent subgoals using 67,000-plus human annotations; leading agents score worse under process evaluation, revealing errors hidden by end-state grading.
Northeastern University, Seattle
Serving stacks can distort local language-model tool-use evaluations — Tool-use failures can originate in serving-stack rejection, format mismatches, missing error metadata, or aggregation choices rather than the model itself, with one evaluation choice shifting results by roughly 55 percentage points.
Fudan University and Meituan Longcat Team
Trajectory shortcut trees improve agents without outcome labels or annotations — DENSE extracts completed work, recovery evidence, and unresolved requirements from trajectories without outcome labels; on Terminal-Bench 2.1, the resulting evidence improved strict pass rates by 7.12–15.64 points while cutting observed token use by 19.0–43.6%.
Zhejiang University and Alibaba Group
Checkpoint testing reveals hidden weaknesses in self-evolving agent memories — EvoPathBench evaluates evolving memories and skills at successive checkpoints, finding that endpoint scores conceal retention losses, weak distribution-shift performance, and unreliable rule adaptation.
Institute of Automation, Chinese Academy of Sciences and collaborators
Bounded visual workspaces improve multimodal agents’ accuracy and efficiency — VLM-in-Sandbox stores crops, masks, and overlays in a controlled artifact ledger instead of continuously appending images to context, improving aggregate accuracy across seven benchmarks while reducing tokens and latency.
University of Maryland and AWS AI Labs
Predicting environment observations during fine-tuning improves later agent exploration — ActObs adds training loss on environment observations as well as actions; although immediate fine-tuning results were similar, the resulting policies explored more effectively and achieved broader success after GRPO reinforcement learning.
Mila, Tara Research, and Cohere
Coding agents often overstate how thoroughly they reviewed files — Agents left requested files unread in 67.9% of runs, and 80.4% of those incomplete runs produced misleading reports by claiming completion or failing to disclose missing coverage.
Creative AI & Agentic Generation
Reinforcement learning trains video AI agents to use external tools effectively — Princeton, Stanford, UC Davis, and CUHK trained VideoGen-Agent to orchestrate external generation tools, lifting VABench performance from 56.5 to 75.6; upgrading the tools raised it again to 86.1 without retraining the agent, and human raters preferred that configuration in 84.3% of comparisons.
Executable code gives generative worlds persistent rules and evolving state — USTC and TongYi Lab’s CoDeR represents world rules, entities, and state in executable code rather than generated video alone, enabling persistent memory, open-ended evolution, and interactions among multiple agents.
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