Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2610.07381v1 Announce Type: new Abstract: Modeling 3D scene geometry and its evolution over time is essential for autonomous driving and robotics. A common paradigm is to use world models to pr...
arXiv:2610.07868v1 Announce Type: new Abstract: Large language model agents have been used to search over symbolic structures such as programs and equations. We propose CueRator, an agentic framework...
arXiv:2610.07969v1 Announce Type: new Abstract: Scaling robotic foundation models requires diverse training data and reliable evaluation environments. Simulation offers a scalable solution, yet exist...
arXiv:2610.08573v1 Announce Type: new Abstract: Cooperative perception improves autonomous driving by sharing complementary observations among vehicles and roadside infrastructure for 3D object detec...
The paper investigates how a small grounding model for a GUI agent should receive the action type. Using Qwen2‑VL‑2B fine‑tuned with LoRA on Android, the authors compare a flat baseline with five conditioning methods—auxiliary loss, hard‑routed action word, additive learned embedding, prepended learned token, and prompt word—under matched data, compute, and decoding settings. Results show that auxiliary loss, additive embedding, and prompt word each improve hit@0.10 by five to seven points over the baseline, while hard routing and prepended token perform similarly to the baseline; however, removing a problematic off‑screen touch point class lifts the baseline by nearly seven points, after which no method outperforms it. "whyItMatters":"The study identifies effective strategies for encoding action type in small grounding models, which could improve the accuracy of GUI agents in real‑world Android applications."
Text2Dashboard is a prototype that converts natural‑language analytic requests into inspectable dashboards for DataBrain. It uses a Codex plugin and an Agent Runtime that enforce schema constraints, read‑only SQL, and deterministic hooks for approval, audit, and failure handling. The system was evaluated on real DataBrain tasks, achieving high success rates on metadata, SQL, and dashboard composition while handling fault scenarios without unintended side effects.
The paper introduces SAGA, a framework that enables large language model agents to evolve by abstracting experiences into reusable principles, procedures, and episodic descriptions. SAGA transforms interaction trajectories into hierarchical knowledge with explicit applicability conditions, linking them back to execution evidence. Experiments on ScienceWorld and ALFWorld show that this execution–abstraction feedback loop improves task performance, and ablation studies confirm the importance of contextual instantiation and action regulation.
EPOCH is an evidence‑governed architecture for AI research agents that improves how candidate discoveries are evaluated and validated. By combining explicit task contracts, typed memory, active falsification, admission checks, and independent replay, EPOCH ensures that each candidate is judged against the strength and scope of its claim. The system achieves state‑of‑the‑art performance on several benchmarks, including AlgoTune, Math14, and AgentHPO, and demonstrates significant advances in executable constructions, algorithm optimization, counterexamples, and proof‑supported results across ten discovery problems.
The paper introduces AMBER, an append‑only memory framework for language‑model agents that interact over long horizons. AMBER lets agents jointly learn to reason, act, and write free‑form memory, guaranteeing retention by construction and enabling end‑to‑end reinforcement learning without extensive curated data. Experiments on WebArena Lite show AMBER outperforms overwrite‑based memory by 4.09 percentage points in average success and improves task completion rates in repeated runs.
The paper presents a vision‑language navigation system that transfers from simulation to a real Ackermann‑steered mobile robot without relying on navigation graphs or panoramic views. It uses a Cross‑Modal Attention architecture trained on simulated data and fine‑tuned with limited real‑world episodes, leveraging linear photometric adjustments and a camera‑LiDAR sensor suite. Evaluation with SPL and nDTW metrics shows robust, adaptable navigation in continuous environments.
MemMux is a local runtime designed to provide runtime verification and honest resource attribution for fleets of parallel coding agents. It emits observable signals that track per‑agent memory usage, ensure complete reclamation of terminated agents, detect escaped child processes, and keep the system from exceeding a bounded memory footprint. In benchmarks against tmux and a raw‑process baseline, MemMux keeps a fleet under a 7.5 GiB budget with zero swap, while ungoverned tools exceed the budget and spill into swap, and it achieves 100 % attribution with low overhead.
The paper investigates how large language model agents can over-rely on agentic memory, a phenomenon where retrieved memories distort inference even when they are correctly stored and retrieved. It shows that memory is helpful when past experience fully transfers to the current task but becomes misleading under partial query-memory overlap, a pattern confirmed by controlled experiments. To address this, the authors propose MEMTRIM, a plug‑and‑play framework that indexes memory evidence at write time and limits its reuse at read time, thereby reducing over-reliance without retraining and preserving useful memory benefits across models and memory architectures.
The paper introduces 2D‑FET‑Bench V2, a benchmark of 128 field‑effect transistor layout tasks derived from real microscopy‑derived flake contours, including complex shapes and multi‑flake scenarios. Each task provides a textual device specification and contour coordinates; agents generate GDSII operations that are verified for geometric, structural, and contour‑integrity compliance. Six models and seven GPT‑based workflow variants were evaluated, with GPT‑5.6‑Luna using ReAct‑3 achieving the highest performance, passing 62.3 % of attempts and solving 80.5 % of tasks at least once.
PsyCIDRA is a dual‑agent framework that couples a free‑form psychiatric interviewer with a diagnostic reasoning agent to support expert review. The interviewer agent uses tools to keep working notes, load expert skills, and pull ICD‑11 references, while the diagnostic agent receives the interview transcript and generates hypotheses with supporting, conflicting, and missing evidence, withholding a final hypothesis if insufficient support exists. In simulations and a blinded human study, PsyCIDRA achieved higher diagnostic agreement and rank‑1 accuracy than direct prompting, indicating its promise for assisting psychiatric assessment through interactive dialogue.
arXiv:2610.07521v1 Announce Type: new Abstract: Driving models increasingly ground reasoning in causal relations, spatial structure, perceptual evidence, and predicted futures. These advances make re...
arXiv:2610.07556v1 Announce Type: new Abstract: Learned orchestration can automatically construct effective language-model multi-agent systems, but existing approaches couple planning to fixed worker...
arXiv:2610.07570v1 Announce Type: new Abstract: In clinical practice, agreement among independent experts is treated as evidence of reliability, and multi-round consensus has become a core mechanism...
arXiv:2610.07578v1 Announce Type: new Abstract: In cooperative Multi-Agent Reinforcement Learning (MARL), agents are often trained under concurrent participation, while in many tasks some agents act...
The paper introduces Personal-Agent Mediated Recommendation, a new paradigm where a personal LLM agent uses cross‑platform user history to adjust a platform’s recommendation ranking. It presents MediateRec, a benchmark for evaluating this mediation, and proposes Personal Attribution Mediation Optimization (PAMO) to balance beneficial rescues against harmful overrides. Experiments show that PAMO improves over outcome‑only reinforcement learning, achieving a better rescue‑harm trade‑off on both synthetic and real cross‑platform tests.
BioStudyBench is a new benchmark that tests AI agents on 25 long‑horizon analysis tasks derived from biomedical studies published between July and September 2026, after the models’ knowledge cutoffs. Each task gives the agent a neutral research question but no data files, requiring it to locate and download relevant public data, search literature using tools limited to pre‑cutoff records, and perform data analysis to report findings. The benchmark shows that providing data and tools improves model performance by an average of 47 percentage points, with closed‑weight models outperforming open‑weight ones (best open‑weight 81.3% vs. best closed‑weight 94.7%).