NeurIPS 2026 论文列表

KV-cache compression for long-horizon LLM agents is often guided by accumulated attention, yet attention frequency can retain repetitive error traces while discarding decisive instructions. We propose TaskKV, a streaming step-level retention policy with three contributions: (1) a signed directional utility label from the first-order change in action likelihood, distinguishing helpful steps from confusing distractors unlike unsigned saliency; (2) a structured residual scorer that treats attention mass and intent alignment as fixed priors and learns only a denoising correction, converging with 500 trajectories; and (3) an entropy-based regime classifier that falls back to contiguous retention when the scorer cannot confidently rank steps. With a 20% KV budget on AgentBench and AlfWorld, TaskKV preserves $\sim$87--89% of full-cache success rate, improving over SnapKV by 5.0--7.9 SR points and over a strong heuristic by 2.9--7.3 points; on dense tasks, gains come from detecting when utility pruning should be disabled. Code is available at https://anonymous.4open.science/r/taskkv-F0D5.

Tool-using language model agents are usually evaluated by whether they finish the user's task. That metric misses a common failure: an agent can complete the requested work while also taking an unauthorized side-effecting action, such as writing to the wrong object, expanding the scope of the request, or contacting an unintended recipient. We call this failure mode collateral damage (CD). We introduce SABench (Side-effect-Aware Benchmark), a benchmark of 201 email, calendar, and ticket tasks. Each task crosses one of four state structures (independent, dependent, externally reachable, cross-tool) with one of four trap families (ambiguity, mis-targeting, cascade harm, overreach), and each task has a deterministic trace-based oracle for both goal satisfaction and authorization compliance. Across 2,211 model-task pairs from 11 frontier LLMs spanning major commercial and open model families, 15.2% of all runs incur CD, with risk concentrated in ambiguity tasks (38.8% violation rate) while non-ambiguous traps average 8.4% CD. Among goal-satisfied runs, 12.8% still produce unauthorized writes. Cross-tool and dependent states have about twice the CD rate of independent states. CD rates vary by 4.7x across models at comparable goal rates, which suggests that task success alone is a poor proxy for safe tool use. The SABench task corpus is released at https://huggingface.co/datasets/anon-sabench-2026/sabench; the traces, oracle, code, and analysis pipeline are released at https://anonymous.4open.science/r/sabench-artifact.

Therapeutic target identification---determining which genes or proteins to modulate for a desired clinical effect---is a foundational step in drug discovery. However, existing computational methods that rank candidates at genome scale face three persistent challenges: (i) static feature design, which discards mechanistic knowledge locked in biomedical literature; (ii) lack of interpretability, offering no auditable gene-level rationale for individual predictions; and (iii) the false-negative problem, where unlabeled but potentially druggable genes are treated as definitive negatives. We propose TargetSage, an LLM reasoning agent for therapeutic target identification built on positive-unlabeled (PU) learning, with three modules. First, Agentic Profiling enriches each gene with LLM-gathered biomedical evidence, distilling 2,261 attributes into a 23-dimensional reasoning vocabulary. Second, Guided Reasoning produces gene-specific rationales and explicit reasoning attributes through a GRPO-trained policy rewarded by downstream Adjusted $F_1$. Third, PU-Aware Scoring integrates structured biological features, explicit reasoning attributes, and implicit reasoning embeddings via gated fusion under an LLM-informed class-prior to produce genome-wide druggability scores. Evaluated on 15 benchmark tasks spanning 19,032 human protein-coding genes, TargetSage consistently outperforms ten baselines, surpassing the strongest by 37% in macro-average Adjusted $F_1$ and generalizing to independent held-out benchmarks. Using only 2021 training data, TargetSage outperforms all ten baselines at ranking genes that gain clinical approval by 2025. On an independent validation set of Phase 2/3 clinical candidates, it further achieves a 51% higher enrichment odds ratio at the top-25 shortlist than the runner-up baseline. TargetSage provides an evidence-grounded, continuously updatable foundation for interpretable target prioritization at genome scale.

How does cooperation evolve in complex agentic systems? Prior work in evolutionary game theory studies why individuals are incentivized to cooperate by isolating social interactions from the physical costs required to execute them, while artificial life models traditionally study the environments that lead to replication without considering the strategic decisions behind the evolved mechanisms. In contrast, we introduce **Autopoietic Game Theory**, a computational model where social interactions, replication mechanisms, and their associated computational costs are endogenous and simultaneously co-evolving. We study these dynamics using a computational substrate of randomly initialized programs in Z80 machine code, showing empirically and theoretically that embedding a social dilemma directly into the physics of computation can favor the emergence of self-replicating, cooperative strategies. When resources are scarce, our analysis shows that *defection can become self-limiting even in well-mixed populations*: parasitic stealing destroys shared energy, slows execution, and can prevent reliable replication. Empirically, evolved programs suppress stealing across several Z80 environments, while spatial assortment further supports structural complexity and task performance. We further show that the framework can incorporate exogenous pressures, such as math tasks structured as sequential social dilemmas, when rewards are tied to computation budgets. These results suggest that coupling an agent's capacity for computation to its available energy transforms cooperation into a dominant scaffolding for building complex, sustainable, and self-organizing systems.

Multi-agent reinforcement learning (MARL) holds great potential but faces robustness challenges due to environmental uncertainty. To address this, distributionally robust Markov games (RMGs) optimize worst-case performance when the environment deviates from the nominal model within a uncertainty set. Beyond robustness, an equally urgent goal for MARL is data efficiency—sampling from vast state and action spaces that grow exponentially with the number of agents potentially leads to the curse of multiagency. However, current provably data-efficient algorithms for RMGs are limited to tabular settings with finite state and action spaces, which are only computationally manageable for small-scale problems, leaving RMGs with large-scale (or infinite) state spaces largely unexplored. The only existing work beyond tabular settings focuses on linear function approximation (LFA) for a restrictive class of RMGs using vanish minimal value assumption and still suffers from sample complexity with the curse of multiagency. In this work, we focuses on general RMGs with LFA. For uncertainty sets defined by total variation distance, we develop provably data-efficient algorithms that break the curse of multiagency in both the generative model setting and a newly proposed online interactive setting. To our knowledge, our results are the first to break the curse of multiagency of sample complexity for RMGs with large (possibly infinite) state spaces, regardless of the uncertainty set construction.

Decentralized Stochastic Approximation (SA) enables cooperative fixed-point solving over mesh networks without a central server. In these systems, local update is relatively cheap compared with coordination, and frequent information exchange makes communication cost the main bottleneck. To alleviate this issue, we propose a decentralized SA method in which each agent performs $H$ local updates before each communication round. By amortizing communications over multiple local SA updates, the method can substantially reduce communication cost. We also study a multiple mixing scheme based on FastMix, which trades a small amount intra-round communications for a stronger consensus effectiveness on poorly connected graphs. Theoretically, we establish finite-time bounds under general contractive norms and Markovian sampling, by combining contraction, consensus error, and delayed Markovian terms in a round-level Lyapunov recursion. The results show trade-offs among computation, communication and sampling. Numerical experiments on decentralized Q-learning, Markovian SGD and linear SA validate the theory and show substantial communication savings for target accuracy.

Data-parallel (DP) load balancing has emerged as a first-order bottleneck in large-scale LLM serving. When a model is sharded across devices via tensor parallelism (TP) or expert parallelism (EP) and replicated across many DP workers, every decode step ends in a synchronization barrier whose latency is set by the most heavily loaded worker; even modest persistent imbalance across DP workers compounds, step after step, into a substantial fraction of wasted compute. The problem is hard for reasons specific to LLM decoding: assignments are sticky (KV caches cannot be migrated), per-request loads grow over time, arrivals are non-stationary, and the router must decide within a sub-100ms decode budget over hundreds of waiting requests and tens of workers. We present BalanceRoute, a family of practical online routing algorithms that target this bottleneck. The first, BR-0, requires no prediction infrastructure and uses a piecewise-linear F-score that captures the sharp asymmetry between admissions that fill safe margin and those that overflow into the envelope; a two-stage decomposition keeps per-step cost compatible with millisecond-scale scheduling. The second, BR-H, generalizes BR-0 with a short, constant lookahead $H$ and a lightweight termination-classifier interface, extending the F-score to a horizon-discounted form. We deploy BalanceRoute on a 144-NPU cluster and evaluate against vLLM baselines on both a proprietary production trace and the public Azure-2024 trace. Across both workloads, BalanceRoute substantially reduces average DP imbalance and improves end-to-end serving throughput. An anonymized open-source release is available at https://anonymous.4open.science/r/BR-Family-on-vllm-acend-0F95/.

Assessing the quality of time series (TS) data is fundamental yet inherently challenging due to the multifaceted nature of quality dimensions. Recently, large language models (LLMs) have emerged as a promising paradigm for TS quality assessment via pairwise comparison and per-dimension evaluation. However, existing approaches rely on manually predefined quality dimensions and purely text-based reasoning, leaving it unknown whether LLMs can identify truly relevant quality dimensions or perform grounded and quantitative quality comparisons. To investigate this, we construct TSQBench, a dedicated benchmark for evaluating LLMs’ capabilities in TS quality assessment, which measures two progressive capabilities: (i) understanding and identifying relevant quality dimensions, and (ii) performing quality comparison under specific dimensions. Our analysis reveals that both open-source and proprietary LLMs consistently struggle with identifying critical quality dimensions and conducting precise, evidence-grounded reasoning over quality characteristics. To address these limitations, we propose TSQAgent, a novel agentic reasoning framework for dedicated TS data quality rating, comprising three collaborative roles: Perceiver for focused dimension selection, Inspector for dimension-wise quantitative analysis, and Adjudicator that aggregates and refines the final judgment. In particular, we introduce an agentic reasoning strategy that instills the ability to identify and prioritize the most relevant quality dimensions, and further propose an agent workflow equipped with external analytical tools to enable precise quantitative comparisons over selected dimensions. Experiments on both the proposed benchmark and eleven real-world datasets demonstrate that our framework not only substantially improves LLMs’ capabilities in quality understanding and quantitative comparison but also effectively translates these improvements into better quality-aware data selection, leading to enhanced downstream performance and data efficiency.

Designing RNA sequences that satisfy target tertiary-structure objectives is an important problem in RNA engineering, with broad applications in synthetic biology and therapeutics. Existing $\mathrm{3D}$ RNA design methods have made substantial progress, but they largely treat the target fold as a geometry-only objective. This is incomplete for RNA, whose functional folds are specified not only by tertiary geometry but also by secondary base-pairing topology. We therefore formulate RNA inverse design as a topology-and-geometry co-design problem and propose $\texttt{TANGO}$, a cooperative multi-agent reinforcement learning framework for this task. $\texttt{TANGO}$ adopts a staged curriculum: it first learns a topology-aware policy from secondary-structure targets, then transfers this policy to tertiary-structure targets to train a general co-design model, and finally applies frontier-guided fine-tuning to refine target-specific trade-offs among secondary-structure fidelity, tertiary-structure fidelity, and sequence diversity. Experiments show that $\texttt{TANGO}$ generates diverse and novel designs, improving tertiary- and secondary-structure performance by $18.3\\%$ and $52.7\\%$, respectively, over the strongest baseline.

When language model agents tackle complex software engineering tasks, they often degrade over long trajectories, which we define as *agent drift*. We focus on two recurring failure modes *overthinking* and *overacting*, i.e., where the agent repeatedly reasons over information it already has, and where it issues tool calls without integrating recent observations or acquiring new evidence. In this paper, we introduce **TACT** (**T**hink-**A**ct **C**alibration via activation s**T**eering), to detect and mitigate agent drift in the residual stream before it surfaces as a behavioral failure. In specific, we label trajectory steps as overthinking, overacting, or calibrated, and find that their hidden states can separate linearly along two *drift axes*, pointing from calibrated behavior toward each failure mode (AUC $\approx$ 0.9). To mitigate agent drift, we project each step's activation onto these axes at test time and pull drifted ones back toward the calibrated region. Experiments show that TACT outperforms unsteered baselines across SWE-bench Verified, Terminal-Bench 2.0, and CLAW-Eval, lifting average resolve rate by $+5.8$ pp on Qwen3.5-27B and $+4.8$ pp on Gemma-4-26B-A4B-it while cutting steps-to-resolve by up to $26\%$. These gains frame agent drift as a steerable direction in the residual stream, and position TACT as a viable handle for reliable long-horizon agents.

Code-switching (CS) involves shifting between two or more languages during a conversation or utterance, a phenomenon often driven by social context and the identity of the speaker. The scarcity of authentic CS data remains a critical bottleneck in developing robust Automatic Speech Recognition (ASR) systems for bilingual communities. While Large Language Models (LLMs) have been employed to synthesize CS data, direct generation approaches typically produce utterances that are pragmatically unnatural and fail to capture the nuanced stylistic variations of real-world bilingual speakers. In this paper, we propose an archetype-driven code-switching dialogue generation framework via agent-based digital cloning. Moving beyond static prompting, our method facilitates autonomous conversations between multi-agent digital clones imbued with specific bilingual archetypes and established linguistic theories. The generation pipeline integrates constrained persona sampling, real-world topic injection, turn-by-turn accommodation dynamics, and multi-dimensional language checking. Applying this framework, we synthesize highly idiomatic bilingual dialogues across 14 diverse language pairs with 260K textual dialogues (1.04M turns of text samples) and convert them into over 1,558 hours of high-fidelity audio data, yielding the SwitchLingua V2 dataset. To rigorously validate our approach and the generated data, we employ a comprehensive evaluation protocol including both advanced LLM assessments and human bilingual expert evaluations. Extensive experiments demonstrate that SwitchLingua V2 produces CS dialogues that are structurally diverse, pragmatically motivated, and socially situated, holding the potential to offer a highly scalable and reliable pathway for advancing multilingual ASR research.

Secure LLM agents should take actions for authorized users in authorized environments. However, current agents' (e.g., OpenClaw) runtimes expose neither user nor environment authorization and existing agent security benchmarks similarly evaluate models without relevant authorization context. We propose SudoBench, a benchmark of 135 paired scenarios in a synthetic consulting universe with a static access control list, spanning user authorization (confused deputy), environment authorization (source-label indirect prompt injection), and both (scope-check indirect prompt injection). For each pair of scenarios, the authorization context is flipped such that an identical prompt should be appropriately complied with or refused. We score each pair as a joint pass requiring the model to produce the correct outcome on both paired scenarios. Across ten frontier closed- and open-weight LLMs, joint pass rate without authorization context sits below 16\% on every model. Adding user and environment authorization raises joint pass to as high as 67--73\%. Even with this authorization context, frontier models remain far from reliably handling contextual authorization, framing authorization context as an important research agenda for agent security.

Long-horizon RTL agents often collapse under context pollution, lacking the active, compartmentalized debugging strategies of human engineers. We introduce OneVeriAgent, a human-like framework modeled directly on expert cognitive workflows. First, mimicking how engineers probe waveform viewers, Agentic Temporal Exploration (ATE) enables dynamic, targeted signal queries, distilling dense simulator dumps into sparse, causal traces. Second, reflecting how designers compartmentalize tasks, a task-scoped Orchestrator enforces strict context isolation, preventing raw tool outputs from polluting the global reasoning trace. On the CVDP benchmark, OneVeriAgent achieves 95.8\% Pass@1 with Claude Opus 4.6, matching state-of-the-art proprietary swarm. Finally, we propose Tool-Validated Self-Distillation (TVSD) to bridge the open-source capability gap. By fine-tuning on self-generated, syntactically valid trajectories without external correctness oracles, TVSD lifts Gemma4-31B-Instruct's Pass@1 from 61.1\% to 74.8\%, demonstrating that fully open-weight RTL agents are viable.

Unsupervised medical anomaly detection aims to identify images or regions that deviate from the learned normal distribution. Reconstruction-based methods are a dominant paradigm, generating a normal-looking reference for each test image and detecting anomalies via input--reconstruction discrepancy. However, most existing methods follow a one-shot reconstruction-and-comparison pipeline, making anomaly scoring vulnerable to reconstruction failures: abnormal regions may be preserved, while normal anatomical structures may be distorted. Moreover, they often lack coordinated control from structural and semantic normality, which can lead to anatomical inconsistency and semantic drift. We propose S2Agent, a structure- and semantic-guided multi-agent framework that reformulates medical anomaly detection as feedback-driven normality reconstruction. S2Agent decomposes the process into three collaborative agents. The Planner derives sample-specific matched-tree structural priors and normal-only semantic claims from normality knowledge. The Reconstructor generates a normality-oriented reference image under their joint guidance. The Detector verifies the reconstruction in matched-tree and semantic-claim spaces, returning feedback to refine structural weights and semantic constraints for the next round. Through this closed loop, S2Agent iteratively corrects structural deviation and semantic drift, suppresses abnormal or unsupported content, and improves the reliability of anomaly scoring. Experiments on three public medical benchmarks demonstrate the effectiveness of the proposed closed-loop guidance.

Communication learning is crucial for complex coordination in multi-agent reinforcement learning (MARL), yet existing methods often struggle with a fundamental dilemma between communication overhead and task performance. Typically designed without theoretical guidance, they produce unstructured and redundant communication graphs, limiting both efficiency and scalability. To address this, we propose Structural Entropy Optimized Communication (SEOC), a framework that directly optimizes inter-agent communication topology using the information-theoretic principle of Structural Entropy. SEOC guides the network to self-organize into a low-entropy state with sparse connectivity and distinct communities, enabling efficient and interpretable decentralized coordination. This is achieved through a unified mechanism that jointly enforces structural order and semantic conciseness, reducing topological complexity via a differentiable structural entropy loss while compressing messages with a node information bottleneck. Experiments on benchmarks such as the StarCraft Multi-Agent Challenge (SMACv2) demonstrate that SEOC significantly outperforms state-of-the-art methods.

As LLM agents advance rapidly in autonomy, the risk of hidden objectives that bypass safety constraints becomes increasingly plausible. Chain-of-thought (CoT) monitoring offers a key control mechanism by reading the agent's reasoning and flagging misalignment before it materializes into action, yet its reliability depends on the realism of the red-team it is stress-tested against. Existing red-teaming efforts apply uniform attack templates that treat the side task as an exogenous insertion, yielding abrupt anomalies any reasonable monitor can flag while leaving the subtler, contextually grounded evasions of a capable agent largely unprobed. We therefore ask: How robust is CoT monitoring against agents that strategically conceal misalignment? To answer this, we introduce TraceMRT (Trace-level Monitor Red-Teaming), an automated red-teaming framework that reframes the side task as a coherent sub-goal of the primary task and searches trace-shaping strategies whose induced trajectories complete the misaligned objective while reading as natural execution. Through black-box attacks on state-of-the-art monitors (e.g., GPT-5.2, and Claude-4.5), we reveal substantial vulnerabilities even in top models. To better counter these vulnerabilities, we further improve monitoring scaffolding with Bayesian Trajectory Monitoring, integrating global context and local evidence to better detect covert misalignment. Empirically, the method significantly consolidates the monitor with 0.91 AUC and 0.74 TPR@FPR=0.05.

In practice, the reasoning strategy often matters more than the model. Rather than calling a fixed large model once per input, a well-designed strategy breaks the problem into steps and uses the right model for each, achieving better accuracy at lower cost. Existing methods have tried to automate strategy design by searching over a fixed set of strategy templates, but the templates themselves limit what reasoning patterns can be discovered. We propose Strategist, a meta-agent that designs a high-performing inference strategy for a given task. Each strategy is a structured executable topology that defines how agents plan, interact, verify, and route across models. Given a task and a small development set, Strategist proposes candidate strategies, runs them on real samples, and revises them against measured accuracy and cost. Every strategy and its components are reusable, forming an evolving library that grows with each new task and compounds what future tasks can build from. On 30 benchmarks, Strategist matches or beats the strongest baselines with an average of 8.5 absolute percentage points higher accuracy at 48% lower inference cost.

Machine learning (ML) predictions are increasingly being used to guide decision-making, giving rise to the problem of decision-focused learning (DFL) where predictors are optimized for downstream decision quality rather than accuracy alone. However, existing work assumes a single decision-maker optimizing in isolation. This paper formalizes *strategic* decision-focused learning, where an ML system predicts an exogenous state that some agents observe before playing a game. For example, a park ranger may predict wildlife locations to allocate anti-poaching patrols against strategic poachers. While the exogenous state is unaffected by agent actions, predictions influence agents' strategies and the resulting equilibrium. We find that strategic considerations fundamentally change the learning problem. In particular, we show the prediction accuracy–equilibrium payoff landscape can be non-monotonic---i.e., better predictions can degrade performance. We propose algorithmic approaches to address these challenges and validate them across benchmarks in wildlife conservation and infrastructure protection. Our theory and experiments highlight the importance of accounting for strategic interactions when designing predictors.

Large language models (LLMs) are increasingly used as interactive agents, but optimizing them for long-horizon decision making remains difficult because current methods are largely purely reactive, which weakens both exploration and credit assignment over extended trajectories. In this work, we present Strategic Trajectory Abstraction (StraTA), a simple framework that introduces an explicit trajectory-level strategy into agentic reinforcement learning (RL). StraTA samples a compact strategy from the initial task state, conditions subsequent actions on that strategy, and trains strategy generation and action execution jointly with a hierarchical GRPO-style rollout design, further enhanced by diverse strategy rollout and critical self-judgment. Experiments on ALFWorld, WebShop, and SciWorld show that StraTA consistently improves both sample efficiency and final performance over strong baselines. StraTA reaches success rates of 93.1% on ALFWorld and 84.2% on WebShop, surpassing the strongest baselines by 2.3% and 11.4% respectively. On SciWorld, StraTA attains a 63.5% overall score, outperforming frontier closed-source models by 6.1% and prior RL baselines by 6.5%.