NeurIPS 2026 论文列表

Multi-view bimanual robot world models must predict future observations while preserving scene identity, following two synchronized but non-exchangeable arms, and maintaining consistency between global and wrist-local views. Existing conditioning schemes often collapse left- and right-arm actions into a view-agnostic control signal, making long-horizon rollout prone to scene drift, wrong-arm responses, and cross-view inconsistency. We propose \textbf{HAI}, a \textbf{Hierarchical Anchored Interaction} architecture for controllable multi-view bimanual world modeling. HAI organizes prediction as a structured information flow. First, hierarchical action-view conditioning routes the bimanual action chunk to camera streams, injects coarse chunk-level intent, performs structured multi-view modeling on the coarse-modulated features, and then injects fine per-frame action control. Second, anchored dynamic generation fuses the fine action-conditioned descriptors with persistent per-view scene anchors before decoding future observations. Experiments on AgiBot and DROID show that HAI improves long-horizon rollout quality over the baselines. Ablations confirm gains in scene stability, wrist-view controllability, and cross-view consistency. Further experiments on policy-improvement diagnostics indicate more useful synthetic rollout signals for downstream VLA policies, achieving an averaging 67.2\% relative gain in success rate. The project page is available at \url{https://hai-anon.github.io/}.

Large Language Model (LLM) agents increasingly serve as personal assistants and workplace collaborators, where their utility depends on memory systems that extract, retrieve, and apply information across long-running conversations. However, both existing memory systems and benchmarks are built around the dyadic, single-user setup, even though real deployments routinely span groups and channels with multiple users interacting with the agent and with each other. This mismatch leaves three properties of group memory unmeasured: (i) group dynamics that go beyond concatenated one-on-one chats, (ii) speaker-grounded belief tracking, where the per-user memory modeling is needed, and (iii) audience-adapted language, where Theory-of-Mind shifts produce role-specific vocabulary. We introduce GroupMemBench, a benchmark that exposes all three. A graph-grounded synthesis pipeline produces multi-party conversations with controllable reply structure and conditions each message on per-user personas and target audiences. An adversarial query pipeline then binds every question to a specific asker across six categories, spanning multi-hop reasoning, knowledge update, term ambiguity, user-implicit reasoning, temporal reasoning, and abstention, and iteratively searches challenging, realistic queries that reflect comprehensive memory capability. Benchmarking leading memory systems exposes a sharp collapse: the strongest one reaches only 46.0% average accuracy, with knowledge update at 27.1% and term ambiguity at 37.7%, while a simple BM25 baseline matches or exceeds most agent memory systems. This indicates current memory ingestion erases the structural and lexical features group memory depends on, leaving multi-user memory far from solved.

Skill-augmented agents increasingly rely on large reusable skill libraries, but retrieving relevant skills is not the same as presenting usable context. Existing methods typically return atomic skills or dependency-aware bundles whose internal roles remain implicit, leaving the agent to infer the execution entry point, support skills, visible requirements, and failure-avoidance guidance. We introduce **G**roup **o**f **Skills** (**GoSkills**), an inference-time group-structured retrieval method that changes the agent-facing retrieval object from a flat skill list to a compact, role-labeled execution context. **GoSkills** builds anchor-centered skill groups from a typed skill graph, expands support groups through a group graph, bottlenecks the selected group plan into a bounded set of atomic skill payloads, and renders a fixed execution contract with **START**, **SUPPORT**, **CHECK**, and **AVOID** fields, without changing the downstream agent, skill payloads, or execution environment. Experiments on SkillsBench and ALFWorld show that \goskills preserves visible-requirement coverage under a small skill budget, improves over flat skill-access baselines, and often improves reward and agent-only runtime relative to structural retrieval references. Code is available at https://anonymous.4open.science/r/Group-of-Skills-E861.

Multi-Agent Debate (MAD) improves the reasoning performance of Large Language Models (LLMs) through multi-round interaction. However, LLMs in MAD are highly susceptible to blind conformity. Existing individual evaluation methods, typically based on confidence or perplexity, fail to reflect the correctness of reasoning and may even exacerbate blind conformity. To address this, we shift the perspective from individual evaluation to group interaction. We define mutual referencing among LLMs as \textbf{Debate Relationships} and recognize that regulating these relationships is the key to mitigating blind conformity. In this paper, we propose a novel framework for \textbf{D}ynamically r\textbf{E}gulating deb\textbf{A}te \textbf{R}elationships (DEAR) from the group perspective. At first, DEAR quantifies consensus and divergence as \textit{group evidence} to capture the debate state. Then, DEAR operates through three stages: 1) What: perceiving group consultation tendency and uncertainty; 2) Who: introducing a Selection RL-Agent to dynamically select reference peers; and 3) How: adopting a Behavior RL-Agent to adaptively adjust generation behaviors.

Long-horizon LLM agents trained with outcome-based RL exhibit a systematic failure mode: reasoning diverges from environmental observations at some step, and the error compounds across subsequent turns. We find that 74\% of failed trajectories contain reasoning–observation inconsistency, and once it appears, 62\% of subsequent turns remain inconsistent. Process rewards reduce but do not eliminate this: under free-form reasoning, agents learn to avoid penalties without genuinely tracking task progress or anticipating action outcomes. We propose ARC (Anchored Reasoning with Commitment), which requires the agent to make two explicit commitments at each step: a global progress statement that reconciles the agent's understanding of task state with accumulated observations, and a local outcome prediction that must be borne out by the next observation. Together, these commitments anchor the agent's reasoning to the actual environment state across turns, and provide well-defined targets for process supervision. To prevent interference between sparse outcome and dense process signals, we decouple their advantages via independent group normalization. On ALFWorld, WebShop, and WebArena, ARC improves over the strongest baseline by 6.4, 3.5, and 9.6 points respectively.

Modeling an opponent’s intent is critical for effective decision-making in non-cooperative, competitive, and general-sum multi-agent reinforcement learning. Existing opponent modeling methods encode intent using an embedding derived from episode information chosen a priori, such as the opponent’s next action or a future environment state, and use this to guide the ego-agent’s behavior. These approaches assume that the chosen information is universally representative of intent; however, we show empirically that this is not the case as intentions are often task- and environment-dependent. To address this, we introduce a task-adaptive opponent modeling framework that learns a performance-driven mixture of multiple intent representations. We further introduce a new intention representation that maximizes mutual information with the ego-agent’s future returns, thereby capturing opponent information that is most directly relevant to performance. Our approach consistently matches or exceeds the performance of state-of-the-art baselines across diverse tasks and yields insights into when and why different opponent modeling strategies succeed.

The emergence of specialized, domain-tuned Large Language Models (LLMs) has demonstrated that smaller models can achieve expert-level performance in specific tasks, while struggling in out-of-domain settings. Current ensemble methods to combine their complementary expertise primarily rely on iterative re-prompting or cross-model refinement. These approaches suffer from high computational costs and latency because they require repeated LLM inference calls. Furthermore, naive aggregation often leads to anchor corruption, in which noise propagated from weaker models degrades the performance of the most accurate expert. To address these challenges, we propose a framework that integrates model predictions at the semantic layer using a bipartite factor graph. In this architecture, individual LLMs are represented as variable nodes, while a set of check nodes assess their consistency based on diverse epistemic criteria. We develop a message-passing protocol inspired by error-recovery systems to resolve disagreements iteratively. Furthermore, we introduce an asymmetric damping mechanism that protects high-reliability anchor nodes from being overridden by the ensemble majority. Unlike existing methods, our approach operates entirely on output distributions and requires no additional LLM calls during the refinement phase. Evaluating on four benchmarks, including MMLU, MMLU-Pro, GPQA, and MedMCQA, our method demonstrates a 97% reduction in token usage and up to a six-fold decrease in API calls, reducing inference time from several minutes to mere milliseconds while consistently outperforming leading multi-agent baselines. These results suggest that graph-based belief propagation is a robust, high-speed, and scalable alternative to the current multi-agent LLM systems. The full pipeline and code will be made public.

Language agents increasingly improve by reusing \emph{skills}---structured procedural artifacts distilled from past experience. In particular, \emph{domain-level} and \emph{model-generated} skills are especially promising. They offer fast adaptation within a domain by encoding domain-specific recurring procedures, and they scale beyond labor-intensive hand-crafting. However, while extraction methods continue to proliferate, understanding remains limited, with no comprehensive study spanning the full skill lifecycle---\textbf{experience generation}, \textbf{skill extraction}, and \textbf{skill consumption}---to ask whether such skills actually work, when they work, and what makes them succeed or fail. To close this gap, we build a utility-grounded evaluation framework that provides systematic experimental results across extractors and target agents, covering five diverse agentic task domains. We find that model-generated skills are beneficial on average but exhibit non-trivial negative transfer, and that neither extractors nor targets behave uniformly. A model can be a strong extractor yet a weak consumer, or vice versa, with skill utility independent of model scale or baseline task strength. To explain these patterns, we then dissect each lifecycle stage in depth, analyzing how experience composition shapes skill quality, what properties characterize useful skills, and how the same skill transfers across different consumers. Finally, we translate these findings into a concrete \emph{meta-skill} that guides skill extraction toward the features tied to actual utility, which consistently improves skill quality across domains and substantially reduces negative transfer.

Agents based on large language models are increasingly expected to autonomously handle negotiation and transactions. However, existing benchmarks predominantly focus on text-only, bilateral, zero-sum price haggling, failing to capture the complexity of real-world commerce. To address this gap, we introduce Agenticpay, a unified framework and benchmark for evaluating how well multimodal agents reach high-welfare, multi-dimensional agreements in realistic markets. Built around four core components ( Environments, Tasks, Agents, and Metrics), Agenticpay comprises 160 multimodal tasks spanning 4 real-world business scenarios (E-commerce, Food Delivery, Ride-hailing, and Apartment Rental) and 8 market topologies, scaling from 1-to-1 bargaining to many-to-many (N-to-N) competitive markets. Beyond price haggling, agents must read product images, infer each opponent's hidden preferences, and trade off multiple binding contract terms (e.g., price, lease duration, return policy, delivery speed). Agents communicate through multi-round natural-language dialogue, with each turn proposing or revising a full contract, and outcomes are scored by a utility-based framework that rewards agreements maximizing joint welfare. Evaluations on state-of-the-art proprietary (GPT-5.4, Claude Sonnet 4.6, Gemini 3.1 Pro Preview) and open-weight (Qwen3-VL-32B-Instruct, InternVL3-38B) multimodal models reveal substantial gaps in non-zero-sum value creation and market reasoning: even the strongest agent (Gemini 3.1 Pro Preview) reaches a GlobalScore of only 42.3/100, and performance consistently degrades as markets scale from bilateral to multi-sided, with the average GlobalScore dropping by 5.7 points and open-weight models suffering the largest declines (up to 11.5 points). These findings establish Agenticpay as a foundational testbed for multimodal agentic commerce. Code and dataset are available at: https://anonymous.4open.science/r/AgenticPay-4BBB/README.md.

Hamiltonian Neural Networks and related structure-preserving dynamics models encode conservation laws, but their scalar Hamiltonian parameterizations inherit the spectral bias of deep networks, limiting their ability to learn stiff multi-timescale systems with coupled fast and slow dynamics. We introduce the Frequency-Structured Hamiltonian Neural Network (FS-HNN), which decomposes the system Hamiltonian into learned components trained on frequency-filtered views of observed trajectories, then recombines them into a single scalar Hamiltonian so that the learned ODE dynamics remain Hamiltonian by construction. For PDEs with unknown or problem-dependent structure, FS-HNN represents Hamiltonians as neural functionals, learns the action of the dynamics operator, and uses projection to enforce conservative structure when appropriate. Across ODE and PDE benchmarks, including the Fermi--Pasta--Ulam--Tsingou chain, shallow water equations, and incompressible Taylor--Green vortex, FS-HNN achieves improved long-horizon rollout accuracy and more accurate energy behavior than existing structure-preserving baselines.

Large Language Model (LLM) interactions are typically underspecified, with users clarifying all necessary details across multiple conversational turns. Yet recent work shows that LLMs perform far worse in this multi-turn setting than in a single turn with all information being available at once, a phenomenon termed ``Lost in Conversation.'' However, bridging this gap effectively and generally remains an open problem. Here we introduce Found in Conversation (FiC), a training framework where a model teaches itself to find back its single-turn competence given underspecified multi-turn prompts. We develop View-Asymmetric Self-Distillation, which runs the same self-distillation backbone on two views of the same information: the teacher sees a single-turn view that concatenates all information revealed across the conversation turns, and the student sees the multi-turn view itself; distillation aligns the student's weaker multi-turn behavior with the teacher's stronger single-turn behavior. Across diverse model sizes (3B–14B) and architectures (Llama, Qwen, Phi, and OLMo), ours achieves a 100% recovery of the single-turn performance on 2 Llama models and recovers over 90% on every model, delivering more helpful and efficient multi-turn conversations without compromising single-turn performance.

Cooperative multi-agent reinforcement learning (MARL) under sparse rewards remains fundamentally challenging because agents often fail to concentrate their influence, leading to insufficiently coordinated exploration. To address this, we propose the Focusing Influence Mechanism (FIM), a framework that encourages agents to focus their influence on under-explored parts of the state space through an entropy-based criterion, while leveraging eligibility traces to enable multiple agents to consistently align and sustain their influence on the same parts of the state space when beneficial, thereby promoting coordinated and persistent joint behavior. By emphasizing under-explored regions of the state space, FIM facilitates more efficient and structured exploration even under extremely sparse rewards. Across diverse MARL benchmarks, FIM consistently improves cooperative performance over strong baselines.

Multi-agent systems (MAS) powered by large language models (LLMs) increasingly adopt planner--executor architectures, where planners convert prompts into subtasks, roles, dependencies, and routing paths. This flexibility enables adaptive coordination, but exposes an attack surface in workflow formation: prompts can shape agent organization without modifying MAS infrastructure. We study this risk through social influence probing workflows to identify high-impact subtasks and malicious-signal propagation. The analysis reveals two vulnerabilities: workflow position can amplify or suppress a malicious signal, and sycophantic framing makes downstream agents more likely to relay it. We translate these findings into FlowSteer, a prompt-only workflow steering attack that converts vulnerability priors into one crafted prompt. FlowSteer aligns a malicious signal with influential task components and guides replanning toward dependencies that preserve propagation. Experiments show that FlowSteer increases malicious success by up to 55% over naive prompting, transfers across MAS setups, and remains effective with black-box topology inference. As FlowSteer biases the planning signals that generate the workflow, MAS defenses that inspect only the generated workflow provide limited protection. As such, we introduce FlowGuard, an input-side defense that reduces malicious success by up to 34% while preserving prompt utility. Our results position workflow formation as a new safety frontier for multi-agent LLM systems, opening a planning-time security perspective on how agent coordination itself can be attacked and defended.

Large language models (LLMs)-based multi-agent systems (MAS) coordinate specialized agents through prompts, tools, and communication topologies, making their hidden workflows valuable intellectual property and security-critical assets. Existing black-box MAS extraction methods implicitly assume that adversarial queries can traverse all agents, which holds for static workflows but breaks down in dynamic workflows whose execution paths depend on input semantics and intermediate states. We identify two key challenges in dynamic workflows: **branch overfitting**, where fully adversarial queries overfit to the same branch and extract only a subset of agents, and **stealthy coverage exploration**, where the adversary needs to achieve complete branch coverage with few redundant queries while not exposing the extraction task. To address these challenges, we propose *FlowLeak* that combines **Task-Preserving Payload Template**, which preserves legitimate task semantics while eliciting workflow information to mitigate branch overfitting, with **Coverage-Guided Branch Exploration**, which uses previously extracted workflow fragments to generate branch-targeted tasks and constrains workflow extraction as an auxiliary task requirement, thereby reducing exploration queries and making extraction harder to identify. Experiments on 102 MAS show that *FlowLeak* addresses both challenges and substantially improves dynamic workflow extraction (2.68$\times$ improvement). Furthermore, we show that the extracted workflow information from *FlowLeak* enhances downstream attacks, highlighting the security risks of MAS workflow extraction and our method.

The Feed-Forward Network (FFN) dominates the activation memory of modern Transformers because the intermediate tensor of width d_ff is materialized in HBM between the two matmul stages. FlashAttention-style I/O-aware tiling avoids the analogous cost in attention, yet has not been brought to FFN. We identify the reason as a hardware-geometry constraint: under one-pass tiled accumulation a fused FFN kernel must keep an output accumulator of width d_model in on-chip SRAM, which is infeasible whenever d_model >= 2048 on currently shipping datacenter GPUs. We formalize this as an SRAM-feasibility proposition and prove a corollary: multi-head decomposition is the simplest architectural transform that brings FFN inside this SRAM-feasible regime, because it makes the per-head accumulator width d_h = d_model/H a free parameter that can be driven below the GPU's SRAM cutoff. Naive multi-head FFN, however, blows up the per-head expansion ratio d_ff/d_h as d_model grows, drifting away from the well-validated SwiGLU 8/3 optimum and degrading quality at scale; we resolve this with a dense MoE-like sub-network correction that restores the optimal ratio while preserving SRAM-feasibility. The resulting architecture, FlashFFN, is an SRAM-feasible FFN block that replaces SwiGLU under matched parameter and FLOPs budgets and derives directly from the SRAM constraint. Across 128M--1.3B parameter models trained on 60--100B tokens of The Pile, FlashFFN achieves a Pareto improvement over SwiGLU at fixed compute: up to ~4x lower peak activation memory at long sequences, inference latency that matches or improves over SwiGLU (up to ~1.32x faster on H100), and consistent quality gains (up to -0.84 in evaluation perplexity and +1.60 average accuracy across six downstream benchmarks at 1.3B)

LLM-based evolution has emerged as a promising way to improve agents by refining non-parametric artifacts, but its wall-clock cost remains a major bottleneck. We identify that this cost comes from synchronized stage execution and imbalance inside each LLM-heavy stage. We present FlashEvolve, an efficient framework that replaces synchronized execution with asynchronous workers and queues, allowing different stages and steps to overlap. To handle data staleness introduced by asynchrony, FlashEvolve tracks artifact versions and applies different policies to update, discard, or patch stale artifacts. Unlike weight-space staleness in asynchronous RL, language-space staleness is inspectable and repairable: a stale artifact is not just delayed work, but readable evidence that the LLM can reflect on, revise, and turn into useful evolution signal. FlashEvolve further improves throughput and token efficiency with speculative stage completion and adaptive workflow control. On GEPA workloads, FlashEvolve improves proposal throughput by 3.5x on local vLLM and 4.9x on API serving over synchronous GEPA. The same design also applies to ACE and Meta-Harness. Our code repository is available at https://anonymous.4open.science/r/FlashEvolve-FEC7/.

We study the problem of average reward Multi-Agent Reinforcement Learning (MARL) where agents interact within a network. Each agent in the network conducts local updates with information within its $k$-hop neighborhood and collaboratively maximizes the overall reward of the entire network. We first provide impossibility results in achieving convergence to the global optimal joint policies in our setting. These impossibility results highlight the challenges of decentralized learning with local information in multi-agent systems, namely the issues of multiagency, where each agent independently optimizes its policy, and partial observability, where agents have limited visibility into the global state. Given these challenges which indicate that decentralized policy optimization can generally be certified only up to stationarity, we provide finite-sample convergence guarantees for a decentralized actor-critic algorithm with linear function approximation that converges to an approximate stationary point with small gradient norm. To overcome such impossibility results, we further show that adding fixed-state entropy regularization reshapes the objective landscape which leads to even stronger global convergence guarantees for our proposed actor-critic algorithm.

Federated Multi-view Clustering (FMVC) enables privacy-preserving cross-view fusion from distributed multi-view data, where each client holds one view of samples and participates in the server's fusion without exposing raw data. In practice, existing FMVC methods face two key challenges: (1) the collected samples often show unaligned correspondence across views, especially under distributed collection; and (2) the gap between higher data privacy and lower computation cost remains hard to reconcile. To address these issues, we propose an Anchor Graph Alignment Framework for Federated Unaligned Multi-view Clustering (**Fed-AGA**), which employs a coarse-to-fine alignment strategy on lightweight anchor graphs to achieve misalignment-robust cross-view fusion while bridging the gap between data privacy and computation cost. Specifically, in the coarse alignment stage, we introduce an anchor-based graph generation module to generate an anchor graph on each client and upload them to the server for category-wise alignment by a designed topology-aware alignment module, which distills topology components as category-exclusive signatures to guide and achieve ideal category-wise alignment. In the next fine alignment and fusion stage, we design an attention-based alignment module to encourage each sample to pay attention to highly similar samples for sample-wise alignment, and the fine-aligned results are contrastively fused into a cross-view consistent anchor graph for clustering. Extensive experiments on multiple datasets demonstrate that Fed-AGA achieves state-of-the-art performance among related methods, while theoretical analysis offers theoretical motivation for two key components.

AI research agents accelerate ML research by automating hypothesis generation, experimentation, and empirical refinement. Existing agent strategies range from greedy hill-climbing to tree search and evolutionary optimization, yet which strategy choices drive performance remains unclear. Answering this question requires a benchmark that separates agent strategy (e.g., search topology) from execution infrastructure (e.g., code editor), so that performance differences are attributable to strategy rather than infrastructure, and that provides process-level metrics beyond final scores to analyze exploration behaviors. Existing benchmarks offer limited support. We propose FML-Bench, a benchmark of 18 fundamental ML research tasks across 10 domains that separates agent strategy from execution infrastructure and defines 12 process-level behavioral metrics. Evaluating six representative agents, we find that: (1) strategy complexity alone does not guarantee strong performance: a simple greedy hill-climber nearly matches the best-performing tree-search agent, both well above the remaining agents; (2) our analysis suggests this pattern relates to improvement opportunity structure: greedy search tends to be more effective when opportunities are dense, while tree-search and evolutionary strategies tend to be more effective when opportunities are sparse; an adaptive agent built on this insight switches to broader exploration upon detecting improvement stagnation and outperforms the other six agents, lending initial support to this observation; and (3) process-level analysis reveals that early convergence and directionally focused exploration are significantly associated with final performance, while solution diversity and compute cost are not. Our benchmark is available at: https://anonymous.4open.science/r/Anonymous-78B6.