All papers

This paper introduces an evaluation framework and reward models to assess how well search agents ground generated answers in retrieved, independent sources.

This paper presents a benchmark to evaluate performance, resource usage, and privacy trade-offs when language model agents collaborate across edge and cloud environments.

This paper presents a compact decision model designed to screen malicious agent tool skills with high recall directly from specification files.

Introduces Socratic Skill Learning, interleaving expert guidance with student practice to produce reusable and transferable agent skills across tasks.

Presents ADMA, an adaptive deliberation controller that dynamically adjusts multi-agent critique and coordination to improve reasoning accuracy.

LatentRemedy uses task-conditioned latent tokens to allow safe key-value cache reuse across conflicting tasks in multi-agent systems.

The paper introduces local contract-conditioned rewriting to prevent sensitive data leakage in email-generating language model agents cost-effectively.

The authors examine why language model agents fail by analyzing discrepancies between internal representations of state values and actual action choices.

The authors present a parallel decoding method that speeds up on-device tool calling for language model agents without requiring model retraining.

The paper presents a unified node-centric framework to quantify and manage multi-agent system lifecycles across design, runtime, and post-hoc attribution.

The paper proposes a training-free framework that co-evolves an executable verifier program and harness state to self-improve agents on open-ended tasks.

The paper proposes a system for physical AI that continuously monitors environments to identify intervention needs and execute timely actions.

Formulates machine unlearning for LLM agents across parametric memory, external persistent storage, and tool-accessible environments.

The paper presents a minimal coding agent baseline for Lean theorem proving using standard bash tooling instead of specialized harnesses.

Proposes a decentralized identity framework using self-sovereign identity principles to establish verification and accountability for autonomous agents.

Distills multi-agent discussions into a single student model by grouping redundant reasoning traces and revisiting unlearned examples.

Co-evolves the user simulator, environment tools, and rewards during training to improve interactive multi-turn language model agents.

Introduces a benchmark of synthetic observability data and incident reports to evaluate automated incident log summarization.

The paper presents a runtime auditor that predicts execution failures in multi-agent systems using loss-guided perturbations to identify hard failure cases.

The paper proposes an ethical governance scaffold based on reflective equilibrium to resolve conflicting instructions and policy rules in agentic workflows.

The paper presents an evolutionary search method that generates small, high-quality test suites to verify code rewrites in self-evolving agents.

Introduces a 35B model, trajectory pipeline, and benchmark unifying search and browser interaction within a single web agent.

Presents a multi-agent framework that allocates test-time computation only to unresolved steps for video spatial reasoning tasks.

Extracts reusable local control signals from frontier model traces to guide smaller language model agents without extra target-model training.

Presents a framework for developing language model facilitators that balance group dynamics, psychological safety, and task goals across multi-party conversations.

Develops an agentic text-to-image framework allowing agents to revisit and select earlier image states for iterative compositional generation.

This paper proposes control-inspired gates to decide when language model agents should adapt their prompts without absorbing noisy user feedback.

This paper introduces an offline compiler that detects conflicts and merges multiple agent skills into a single coherent capability.

The paper presents a benchmark evaluating how language model agents handle unauthorized tool call retries after receiving timeouts.

The paper presents Google Earth AI's geospatial reasoning agent along with evaluation benchmarks for complex multi-step geospatial workflows.

The paper presents a self-improvement method where coding agents refine natural-language behavioral specifications using execution-verified tests.

This paper investigates catastrophic forgetting in continual agent skill learning and proposes a factorized memory architecture to preserve procedural execution.

This empirical study evaluates how compact versus full state representations affect the ability of language model agents to learn long-horizon workflows.

This paper introduces a self-evolution framework that trains visual agents to invoke tools using reinforcement learning and unlabeled images.

This paper evaluates the capabilities and limitations of using activation steering vectors as an agent memory mechanism.

This paper introduces a benchmark that uses agent skill specifications to evaluate the reliability and procedural correctness of AI agents.

This paper presents a framework for discovering and synthesizing terminal-based agent tasks grounded in real-world artifact transitions.

The paper proposes an execution-gating method to safely launch and cancel tool calls early during live speech recognition before user self-corrections.

The paper presents techniques to adapt and quantize a small language model for on-device deployment in phone call agent services.

The paper introduces a dual-process memory framework coordinating small and large models for efficient long-context reasoning.

The paper evaluates the ability of language model agents to diagnose appliance faults using a simulated appliance benchmark based on sensor data.

Proposes a structured knowledge admission and verification system to prevent continual learning agents from storing harmful past experience entries.

The paper proposes a framework that replaces generative model calls with lightweight decision models for routing and reviewing in multi-agent workflows.

The paper presents a framework to evaluate whether language model agents can build lower-cost benchmark tests that match reference evaluations.

The paper proposes a policy optimization method that treats reasoning transitions as learned coordinates in an energy space to internalize self-correction.

Introduces a self-supervised consistency loss across varied tool interfaces to make language model agents robust against notation and naming changes.

The paper proposes a runtime monitoring method to detect and correct language model agents that deviate from intended procedures without reporting it.

The paper measures whether requester users can understand multi-step agent replies and evaluates how explicit constraints improve reply usability.

The paper learns trajectory embeddings to predict the success or failure of language model agents early during execution.

The paper proposes a tree search method that optimizes agent skills through contrastive atomic rule editing and execution memory.

This paper proposes a self-play reinforcement learning framework that automatically generates tasks and intermediate verifiable rewards for long-horizon agent training.

This paper presents a distillation method where a teacher model minimally edits a student web agent's reasoning traces at failure points.

This paper introduces a benchmark evaluating whether coding agents can reimplement and train biological AI tools from published scientific papers.

This paper proposes a multi-agent system designed to reduce clinical medication errors through dynamic collaborative coordination.

The paper introduces an evaluation framework that diagnoses bottlenecks and dynamically regenerates benchmark tasks for language model agents.

The paper presents an evaluation framework to test the interactive agentic capabilities of base language models before post-training.

The paper presents a Lean 4 runtime allowing self-extending agents to run verified computations across tools without constant human oversight.

The paper introduces an event ledger and executable aggregation framework to help agents accurately query and summarize trajectory history.

The paper introduces a discrete codebook representation that pairs associative indexing with vector search for long-term agent memory.

The paper investigates context-aware model routing rules across turns in stateful multi-step agent applications.