Tool Use & Agents

Proactive agents can now be rigorously evaluated in real-world scenarios, revealing critical insights into their performance drivers.

Reward functions that generalize across environments can be learned more effectively by strategically leveraging heterogeneous feedback modalities, leading to substantial improvements in agent performance.

Watermarking LLM-agent trajectories just got a major upgrade—TRACE achieves near-perfect detection without sacrificing performance, even under aggressive adversarial conditions.

Harnesses can evolve in real-time during evaluation, leading to significant performance gains without retraining the underlying model.

Workflows can now be seen as dynamic knowledge objects, not just static processes, revolutionizing how we manage LLM interactions.

Memory compaction in LLMs is fundamentally flawed, with critical information often discarded before it's needed, revealing a systemic inefficiency across all layers.

WebSwarm's innovative recursive delegation allows agents to not only search but also adaptively collaborate, leading to superior performance in complex web search tasks.

Trustworthy agentic AI evaluation in decentralized energy markets hinges on balancing market utility and safety, revealing critical vulnerabilities in reward-maximizing strategies.

Users who depend on manual context attachment experience a dramatic drop in task success, revealing a critical divide in AI utility that could reshape how we design intelligent systems.

Balancing session-centric scheduling can boost LLM cluster throughput by up to 16% without sacrificing token reuse.

Agents can now work independently on data changes while humans maintain oversight, revolutionizing collaborative data management.

Shifting the error landscape in compliance management, this pipeline reveals that a single misidentified asset can lead to irrelevant vulnerabilities, making risk assessment more visible and manageable.

Invisible perturbations can lead to a staggering 75.8% information loss in agentic crawlers without altering the human-visible content.

Compact and informative schemas generated by ASMR can revolutionize the way ship maintenance reports are authored, leading to more actionable insights.

Prismata cuts attack success rates dramatically while ensuring web agents can still perform their intended tasks without developer input.

TokenWall slashes the attack success rate to 12.5% while ensuring a 97.4% pass rate for benign interactions, all with just 0.69 seconds of added latency.

Analysts trust AI predictions but still rely heavily on traditional methods, highlighting a critical gap in AI integration for crime linkage analysis.

Self-evolving LLM agents can slash latency by up to 62% while significantly boosting reliability in industrial applications.

A proactive memory agent can significantly enhance decision-making in long-horizon tasks by preventing critical information from being forgotten.

CausalDS reveals that LLMs can navigate complex causal reasoning tasks while effectively managing uncertainty and abstention, a critical skill for real-world data analysis.

Bypassing database drivers can lead to up to 27x speedups in analytical workloads by directly reading storage files with LLM-assisted code synthesis.

LLM-generated skills fail to outperform basic task prompts in data science workflows, challenging the assumption that automated skill generation enhances AI performance.

Penalizing the decision-making path while rewarding the outcome can drastically reduce operational violations in real-world agent interactions.

Transforming data systems from passive repositories into active agents could redefine the landscape of autonomous automation and its safety protocols.

Easier tasks can sabotage the learning of harder tasks in multi-task RL, but a new entropy-aware optimization strategy can turn this challenge into an advantage.

With over 216,000 skills sourced from both academic and community contributions, SkillCenter transforms the landscape of operational knowledge for autonomous AI agents.

Structural designers thrive on friction, and interactive AI can enhance creativity by preserving the reflective challenges of the design process.

Over 40% improvement in analytical efficiency could revolutionize how researchers conduct trajectory inference in single-cell transcriptomics.

The orchestration layer can slash AI task costs by over 40% without sacrificing quality, fundamentally reshaping how enterprises approach agentic AI deployment.

Taint-style vulnerabilities in MCP servers are not only common but also require innovative mitigation strategies like SPELLSMITH, which outperforms conventional fixes.

A new seven-level harm scale reveals hidden vulnerabilities in AI agent defenses that binary metrics overlook, exposing risks even when attack-success rates appear low.

STRACE transforms noisy execution traces into precise optimization signals, leading to a 42.5% to 58.5% success rate improvement in agent performance.

Concentrating model capacity on delegation roles can yield substantial performance gains in hierarchical search agents, revealing a critical bottleneck in task decomposition.

Tool over-calling can be reduced by nearly 4% through a novel calibration method that reveals the hidden dynamics of token-level influences in multi-teacher training.

Self-evolving LLM agents can drastically reduce reasoning overhead by transforming atomic actions into reusable Standard Operating Procedures, leading to higher success rates and fewer interaction rounds.

Attackers can exploit LLMs' tendency to hallucinate resource identifiers, enabling scalable untargeted promptware attacks that could establish a botnet.

The unique challenges posed by agentic AI demand a fresh governance approach, as traditional frameworks may no longer suffice.

Silent policy violations in tool-using LLMs can be mitigated by deterministic gates, improving success rates by over 12 percentage points in critical tasks.

Transforming AI agent exploration into a deterministic workflow can slash operational costs by over 70% while doubling incident handling capacity.

Achieving a staggering 96.5% human acceptance rate, EmbodiedGen V2 transforms how we create and utilize 3D environments for embodied AI training.

Agents can significantly expand their capabilities through cooperative affordances, transforming how we design multi-agent systems in robotics.

FRAMe achieves up to 99% validity in easy scenarios, showcasing how LLMs can seamlessly align autonomous flight planning with human preferences.

Optimization performance varies significantly by workload, challenging the notion that larger models are always superior in coding tasks.

AI's role in code review is not a simple enhancement; it hinges on human expertise and the review process structure, revealing a complex interplay that challenges prevailing assumptions.

Static safety policies fail in offensive security, with ScopeJudge revealing that context-aware monitoring is crucial to avoid costly violations.

AgentLocate reveals not just which agent failed, but also the critical moment when the system first went off track, outperforming traditional methods in efficiency and accuracy.

Verifiable environments can empower web agents to self-evolve, achieving competitive performance without the need for external teacher models.

Jet-Long achieves a remarkable balance between short-context fidelity and long-context performance, outperforming leading models while remaining hyperparameter-resilient.

Single-rollout sampling can dramatically improve the stability and effectiveness of RL training for large language models, outperforming traditional methods by a significant margin.

TurnOPD redefines on-policy distillation by optimizing training budgets at the turn level, leading to superior agent performance without increasing training time.