NeurIPS 2026 论文列表

In this paper, we introduce Exact Flow Linear Attention(EFLA), an exact-flow formulation of delta-rule linear attention. We show that the delta-rule update can be interpreted as an explicit Euler discretization of an underlying continuous-time system. EFLA replaces this first-order update with the exact closed-form flow. By exploiting the rank-1 structure of the dynamics matrix, both the matrix exponential and the input integral collapse to a simple update that preserves delta-rule linear attention's algebraic structure, parameter count, linear-time complexity, and chunkwise parallelism. This attention mechanism removes the Euler discretization error of the delta-rule dynamics without introducing additional parameters. Experiments on robustness tests, language modeling benchmarks, and the MAD synthetic benchmark show that EFLA improves stability under corrupted and high-energy inputs, reduces perplexity, and achieves stronger downstream performance compared to SSM and Euler-style baselines. These results establish exact-flow integration as a principled and scalable update mechanism for delta-rule linear attention.

Diffusion Transformers (DiTs) have emerged as the state-of-the-art architecture in high-fidelity generative modeling, but their massive computational demands and multi-step inference process limit their deployment in practical scenarios. Post-training quantization (PTQ) offers a promising solution for accelerating inference, but DiTs suffer from severe activation outlier issues, which can lead to catastrophic accuracy loss in integer formats. Although some recent outlier mitigation methods employ orthogonal transformations or diagonal scaling to smooth the activation distribution, they either solve the transformation matrix solely for activations or fail to fully balance the inter-channel variance between activations and weights. Additionally, the construction of the calibration set by taking the weighted average of the activation values for each time step introduces spurious temporal correlations. To address these issues, we propose a joint diagonalization of the activation-weight covariance matrix based on the solution of a generalized eigenvalue problem (GEVP). We also transform the feature fusion of the calibration set's temporal dimension into probabilistic sampling and assembly in the spatial dimension using Uniform Token Splicing (UTS). Specifically, compared to suboptimal baseline methods, our approach reduces the FID by 16.06 and 6.49 on both the W3A4-quantized DiT-XL/2 and PixArt-$\Sigma$ models, respectively. This demonstrates that our method successfully suppresses outliers in activations and weights, maintaining DiT's performance under low-bit quantization conditions. Code is available at the anonymous repository \url{https://anonymous.4open.science/r/JDUS-DiT-74E1}.

A common failure mode in long-horizon agentic test-time scaling is error propagation, where factual errors or invalid deductions introduced at intermediate steps persist in the agent's belief state and contaminate later reasoning. Existing test-time scaling methods provide limited control over this process, as they often rely on agents to detect their own mistakes, select among flawed trajectories, or refine solutions only after errors have already shaped the reasoning path. We propose ExComm, a communication protocol for exploration-stage agentic test-time scaling. ExComm is motivated by the empirical observation that the majority of intermediate errors in parallel agentic reasoning produce detectable cross-agent factual conflicts. Leveraging the iterative structure of agentic workflows, ExComm periodically audits agent belief states to detect such conflicts, resolves them through a dedicated tool-based verification loop, and returns concise, targeted feedback to the involved agents. Corrections are incorporated through soft belief updates, which append verified feedback rather than overwriting existing beliefs. Furthermore, to prevent collapsing trajectory diversity due to communication, ExComm further introduces a trajectory diversification module that redirects redundant trajectories toward orthogonal strategies. Experiments on AIME 2024, AIME 2025, and GAIA with Gemini-2.5-Flash-Lite and Qwen3.5-4B show that ExComm consistently outperforms strong test-time scaling baselines, achieving average performance gains of 5.7\% and 5.0\% over the best-performing baselines, respectively. Further analyses demonstrate improved error recovery, favorable scaling behavior, stronger diversity than adapted communication baselines, and the best performance-cost trade-off among the evaluated methods.

Building agentic systems that can autonomously self-improve from experience is a longstanding goal of AI. Large language models (LLMs) today primarily self-improve via two mechanisms: self-reflection for context updates, and reinforcement learning (RL) for weight updates. In this work, we propose Evolutionary System Prompt Learning (E-SPL), a method for jointly improving model contexts and model weights. In each RL iteration, E-SPL samples trajectories under multiple system prompts in parallel, then jointly applies RL updates to weights and evolutionary updates to system prompts via LLM self-reflection. E-SPL encourages a natural division between declarative knowledge encoded in prompts and procedural knowledge encoded in weights. Most notably, in an easy-to-hard generalization setting (AIME $\rightarrow$ BeyondAIME), E-SPL improves RL success rate from 38.8% $\rightarrow$ 45.1%. E-SPL also improves RL on AIME 2025 (56.3% $\rightarrow$ 60.6%), HMMT 2025 (50.0% $\rightarrow$ 52.7%), and agentic search (44.2% $\rightarrow$ 48.6% on gpt-oss-120b). Across all settings, E-SPL outperforms both RL-only and evolution-only baselines, demonstrating that weight updates and context updates are deeply synergistic and can together yield gains in generalization that neither achieves alone.

Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture? Large Language Models (LLMs) integrated into evolutionary search have recently produced state-of-the-art solutions on optimization tasks, including open mathematical conjectures, GPU kernel design, scientific law discovery, and combinatorial puzzles. To achieve this, prior work applied search scaffolds to one target task at a time, so every new problem is approached from scratch and the experience accumulated during search is discarded once the model finishes its attempt. This leaves the capability of iteratively evolving a solution (\textit{e.g.}, knowing which part to mutate and how, deciding when to backtrack) entirely in the scaffold rather than in the model itself. Whether the model itself could acquire this capability and reuse it across different tasks has been largely unexamined. To address this, we introduce \textbf{\textsc{Evolution Fine-Tuning} (EFT)}, a mid-training paradigm that teaches LLMs to evolve solutions across tasks by converting evolutionary search trajectories into supervision. We construct \datasetName, a 156K-trajectory dataset spanning 10 domains and 371 optimization tasks, and fine-tune open-source LLMs from 2B to 9B parameters. Empirically, EFT confers cross-task generalization: across 22 held-out tasks, our models surpass their base counterparts by 10.22\% on average. Furthermore, when paired with test-time RL, our model match state-of-the-art performance on two circle-packing tasks and outperforms its base-model counterparts on the Erdős minimum-overlap problem. EFT thus serves as a ``practice phase'' for general-purpose discovery agents that doesn't solve new problems from scratch.

Automating optimization modeling from natural language faces two key challenges: training corpora lack structural diversity, and data generation pipelines remain static and decoupled from model learning. To address these challenges, we propose EvoOptiGraph, a novel framework where data and model co-evolve, driven by model weaknesses. EvoOptiGraph represents each mixed-integer linear program (MILP) as an attributed bipartite graph and applies validity-preserving evolutionary operators to generate structurally diverse instances. The evolved graphs are converted into solver code and natural language via deterministic compilation and verified back translation. Training proceeds in two stages: supervised fine-tuning (SFT) on an initial dataset, followed by reinforcement learning with verifiable rewards (RLVR), where graph-derived weakness signals dynamically evolve new instances that target the model's failures, forming a closed loop that continuously updates the training distribution. Empirical results on six public datasets show that EvoOptiGraph significantly outperforms larger generalist models, agentic methods, and specialized baselines in accuracy, executability, and generalization. These results demonstrate that targeted data–model coevolution is an effective strategy for improving LLMs on optimization modeling tasks.

Recent benchmarks for Large Language Model (LLM) agents mainly evaluate reasoning, planning, and execution. However, memory is also essential for agents, as it enables them to store, update, and retrieve information over time. This ability remains under-evaluated, largely because existing benchmarks do not provide a systematic way to assess memory mechanisms. In this paper, we study agent memory from a self-evolving perspective and introduce \texttt{EvoMemBench}, a unified benchmark organized along two axes: memory scope (in-episode vs.cross-episode) and memory content (knowledge-oriented vs.execution-oriented). We compare 15 representative memory methods with strong long-context baselines under a standardized protocol. Results show that current memory systems are still far from a general solution: long-context baselines remain highly competitive, memory helps most when the current context is insufficient or tasks are difficult, and no single memory form works consistently across all settings. Retrieval-based methods remain strong for knowledge-intensive settings, whereas procedural and long-term memory methods are more effective for execution-oriented tasks when their stored experience matches the task structure. We hope EvoMemBench facilitates future research on more effective memory systems for LLM-based agents. Our code is available at \url{https://anonymous.4open.science/r/EvoMemBench-2}.

Multimodal large language model (MLLM) agents increasingly operate over long histories of dialogues, images, and evolving facts, yet finite context windows and weak persistent memory cause forgetting, temporal inconsistency, and loss of precise past evidence. Existing multimodal memory agents either discard visual evidence by reducing images to captions or treat memory as a static store that is built once and never revised during reasoning; self-evolving agents further accumulate experience via prompting without tight coupling to retrieval and memory revision. Closing this loop with reinforcement learning additionally suffers from credit assignment difficulties over long, multimodal action trajectories. To address these challenges, we propose EvoMM, a reinforced self-evolving multimodal agentic memory framework that unifies memory construction, retrieval, and evolution within a single agentic loop. EvoMM maintains a mutable two-level memory of turn-level short memories and session-level long summaries, organized by a heterogeneous multimodal memory graph with temporal, semantic, image-co-reference, and keyword edges. An iterative PLAN→RETRIEVE→REFLECT→ANSWER policy then revises long memory on the fly and distills structured retrieval experience into a cross-query store for reuse. To optimize this policy, we further introduce PRR-GRPO (Planning-Retrieval-Reflection GRPO), which augments outcome rewards with gated, action-aware process rewards and performs dense credit assignment over a memory-action tree with dual-scale advantage normalization. Experiments on Mem-Gallery, MMLongBench, and LoCoMo show that EvoMM consistently outperforms strong text-only, multimodal, and agentic memory baselines, establishing a training paradigm for memory that reads, revises, and evolves with each query.

Human experts can accumulate experience from open-ended and complex inspection tasks, thereby continually improving their inspection capabilities. Actually, industrial hardware inspection faces challenges including open-set defect categories, complex defects (tiny, high-gloss, low-light, or blurred), high inference cost, and difficulty in accumulating experience post-deployment. Existing end-to-end inspection methods typically couple image restoration with defect recognition in a single forward pass, limiting generalization to unseen components or composite defects; cascade “restore-then-detect” approaches often rely on static preprocessing, hindering adaptive adjustment based on scenario, defect morphology, and historical failures. To address this, we propose EvoInspect, the first memory-enhanced multi-modal multi-agent framework for industrial defect object detection. It leverages a multi-modal agentic memory mechanism to distill self-evolving scenario-specific Defect Detail Restoration decisions and dynamically grounded trajectories, enabling “accumulating experience like human experts.” Specifically, (1) we introduce a dynamic Defect Detail Restoration strategy, including adaptive slicing for tiny defects, highlight suppression, low-light enhancement, super-resolution, and binarization, combined with a coarse-to-fine token-efficient grounding mechanism for efficient defect capture; (2) we design a dual-channel distillation mechanism unifying restoration and detection (perception and execution) experience, incorporating both successful and failed validation trajectories to support system self-evolution. Experiments on bearing surfaces, PCBs, solar electroluminescence (solar EL), and magnetic tile datasets show that EvoInspect consistently outperforms strong baselines in defect localization recall, inference accuracy, while demonstrating cross-scenario adaptability and offline continual improvement.

Video temporal grounding (VTG) takes an untrimmed video and a natural-language query as input and localizes the temporal moment that best matches the query. Existing methods rely on large, task-specific datasets requiring costly manual annotation. We introduce EvoGround, a framework of two coupled self-evolving agents, a proposer and a solver, that learn temporal grounding from raw videos without any human-labeled data. The proposer generates query--moment pairs from raw videos, while the solver learns to ground them and feeds back signals that improve the proposer in return. Through this self-reinforcing reinforcement-learning loop, the two agents are initialized from the same backbone and mutually improve across iterations. Trained on 2.5K unlabeled videos, EvoGround matches or surpasses fully supervised models across multiple VTG benchmarks, while emerging as a state-of-the-art fine-grained video captioner without manual labels.

High-fidelity diagram creation requires coordinated visual-spatial decisions over semantic topology, visual styling, and geometric layout. Existing methods face a representation gap: pixel-based generation offers limited object-level control, while code-based synthesis provides executable structure at the cost of intuitive manipulation. We introduce EvoDiagram, an agentic framework for editable diagram creation through an intermediate canvas schema that is both machine-actionable and directly manipulable by users. EvoDiagram first constructs a diagram manifest through coordinated structure, style, and layout agents, and then renders the manifest in a diagnostic verification environment. This environment produces objective defect signals and VLM-based critique, enabling localized refinement of the current diagram while also providing evidence for long-term design expertise evolution. The evolution mechanism distills refinement traces into a three-tier design expertise memory, where candidate strategies are linked to the verification evidence that supports them and are promoted into broader guidelines and principles only through repeated cross-context support. We further define CanvasBench, a canvas-recoverable benchmark and evaluation protocol with content, visual, and cognitive dimensions. The current manuscript focuses on the framework, benchmark design, and reporting protocol; empirical results should be added only when the corresponding artifacts, judge traces, and analysis scripts are available for verification. Our code is available at \href{https://anonymous.4open.science/r/evo-diagram/}{https://anonymous.4open.science/r/evo-diagram/}.

Coding agents are increasingly used as iterative development partners, but most benchmarks still evaluate one specification followed by one final assessment. This fails to capture whether agents can maintain executable artifacts as requirements evolve. We introduce EvoCode-Bench, a benchmark of 26 stateful coding tasks spanning 227 evaluated rounds. Each task preserves the agent's workspace across 5--15 rounds, expresses requirements through observable behavior, and uses cumulative executable tests to check both new requirements and regressions against still-active prior ones. We evaluate 13 coding agents with two complementary metrics: MT@4, a four-attempt fail-stop multi-round score, and SR, a single-round score from a reference-completed prior state. For most agents, SR exceeds MT@4 by 22--40 points, indicating that isolated instruction-following does not imply persistent reliability. The gap also changes model rankings: the highest-SR agent (78.9) ranks only third in persistent execution (44.0 MT@4). Even the strongest agents achieve only \~50\% success on multi-turn metrics, and aggregate pass rate drops below half of round-1 performance by round~5. Failure analysis reveals tier-dependent behavior: weaker agents fail early, while stronger agents survive long enough to expose specification-tracking and regression failures. These results show that single-round and persistent multi-turn coding evaluate different capability dimensions. We release the benchmark data and Harbor multi-turn infrastructure.

The development of native computer-use agents (CUA) represents a significant leap in multimodal AI. However, their potential is currently bottlenecked by the constraints of static data scaling. Existing paradigms relying primarily on passive imitation of static datasets struggle to capture the intricate causal dynamics inherent in long-horizon computer tasks. In this work, we introduce EvoCUA, a native agentic model that integrates data generation and policy optimization into a self-sustaining evolutionary cycle. This approach employs a verifiable synthesis engine to autonomously generate diverse tasks with executable validators, alongside a scalable infrastructure orchestrating tens of thousands of sandbox rollouts for mass experience acquisition. To internalize this experience, our iterative evolving learning strategy reinforces successful routines while transforming failure trajectories into rich supervision through error analysis and self-correction.EvoCUA achieves a 56.7\% success rate on the OSWorld benchmark, establishing a new state-of-the-art for open-weights models across multiple model scales. It significantly outperforms the previous best open-source model, OpenCUA-72B (45.0%), and surpasses leading closed-weights models such as UI-TARS-2 (53.1%), offering a highly parameter-efficient and reproducible alternative to massive closed-weights models. These results demonstrate the generalizability of the evolving paradigm across foundation models of varying scales, establishing a robust and scalable path for advancing native agent capabilities.

Vision-Language-Action (VLA) models have emerged as a promising paradigm for robotic manipulation by unifying perception, language grounding, and action generation. However, they often struggle in scenarios requiring precise spatial understanding, as current VLA models primarily rely on 2D visual representations that lack depth information and detailed spatial relationships. While recent approaches incorporate explicit 3D inputs such as depth maps or point clouds to address this issue, they often increase system complexity, require additional sensors, and remain vulnerable to sensing noise and reconstruction errors. Another line of work explores implicit 3D-aware spatial modeling directly from RGB observations without extra sensors, but it often relies on large geometry foundation models, resulting in higher training and deployment costs. To address these challenges, we propose \textbf{Evo-Depth}, a lightweight depth-enhanced VLA framework that enhances spatially grounded manipulation without relying on additional sensing hardware or compromising deployment efficiency. Evo-Depth employs a lightweight \textit{Implicit Depth Encoding Module} (IDEM) to extract compact depth features from multi-view RGB images. These features are incorporated into vision-language representations through a \textit{Spatial Enhancement Module} (SEM) via depth-aware modulation, enabling efficient spatial-semantic enhancement. A \textit{Progressive Alignment Training} strategy is further introduced to align the resulting depth-enhanced representations with downstream action learning. Extensive experiments in simulation and real-world settings demonstrate the effectiveness of Evo-Depth. With only \textbf{0.9B} parameters, Evo-Depth achieves state-of-the-art-level performance across four simulation benchmarks. In real-world experiments, Evo-Depth attains the highest average success rate while also exhibiting the smallest model size, lowest GPU memory usage, and highest inference frequency among compared methods. These results demonstrate that lightweight implicit depth enhancement is an effective and practical solution for spatially grounded robotic manipulation. We release code and models to facilitate future research on lightweight depth-enhanced VLA models.

Free-form language generation poses a challenge for uncertainty quantification because responses do not belong to a fixed global label space. Semantic-entropy methods address this issue by sampling responses, clustering them by meaning, and estimating uncertainty over prompt-specific semantic classes. However, they mainly measure semantic dispersion and do not provide a principled decomposition into aleatoric and epistemic components, which in our setting correspond to ambiguity among plausible semantic meanings and insufficiency of model support, respectively. Moreover, cluster frequencies depend on the sampling budget and should not be treated as evidential strength. We propose a prompt-level evidential probing framework for semantic uncertainty decomposition in LLM generation. For each prompt, sampled responses define a dynamic semantic frame and provide initial support for the corresponding semantic classes. A lightweight evidence-retention probe learns how much of this support should be retained as reliable semantic evidence. The retained evidence induces a subjective opinion in which dissonance captures conflict among supported semantic alternatives and vacuity captures insufficient retained evidence. To train the probe, we introduce Augmented Semantic Uncertainty Cross-Entropy (AS-UCE), an LLM-specific evidential objective that augments the prompt-specific semantic frame with an unsupported class, allowing unreliable or hallucinated support to be routed away from observed semantic clusters during training. Experiments across four LLM backbones show that our framework consistently improves task-relevant AUROC and AUPR, with average AUROC gains of 7.4\% for OOD detection and 4\% for ambiguity detection over the competitive baselines, while requiring fewer sampled responses than perturbation-based decomposition methods.

Vision-large language models (VLLMs) editing aims to efficiently update model responses while retaining generalization and unrelated knowledge. Recent online editors store previous edits as reusable experts, forming a mixture-of-experts editing memory for scalable VLLM correction. By retrieving relevant experts at inference time, this paradigm avoids repeatedly overwriting model parameters and enables continual reuse of accumulated editing experience. However, existing sample-level residual experts usually encode each edit instance as a whole, entangling target-relevant visual evidence with irrelevant context. This coarse expert construction weakens expert reusability, leads to inaccurate routing, and increases unintended side effects during continual editing. To address this limitation, we reformulate online VLLM editing as evidence guided expert construction and routing. A fine mask identifies edit-relevant visual evidence, from which support-suppress experts are constructed to strengthen target consistent cues and weaken edit conflicting cues. Each expert is stored with routing signals and reliability scores, and later retrieved through joint sparse routing over visual evidence, textual context, and expert quality. Experiments demonstrate consistent improvements in editing reliability, generalization, and interpretability over competitive online editing baselines.

Referring Remote Sensing Image Segmentation (RRSIS) aims to segment target objects from remote sensing images based on natural language descriptions, requiring precise vision–language alignment under significant ambiguity. Despite the strong generalization ability of Segment Anything Model 3 (SAM3), the large spatial extent and varying scales of targets in remote sensing images intensify the ambiguity of referring expressions. This poses a major challenge for fine-grained segmentation. To address the challenge, we propose **EviSAM3**, an evidence-driven parameter-efficient adaptation framework that mimics human decision-making by dynamically accumulating partial evidence. Specifically, EviSAM3 progressively integrates cross-modal evidence through an evidence memory bank, where each incoming piece is aggregated via momentum-based updates. As evidence accumulates, the model performs iterative evidence rectification to resolve ambiguity under incomplete observations. Meanwhile, the contributions of different evidence are adaptively modulated based on their diagnostic relevance, allowing more informative evidence to dominate the reasoning process and guide the final prediction. Extensive experiments demonstrate that EviSAM3 consistently outperforms state-of-the-art methods, particularly in challenging scenarios with high ambiguity and significant scale variation. The code is available in the supplementary material.

Creating images from noise is image generation; reconstructing fine details from coarse inputs is super-resolution. Despite their practical differences, both can be understood as reversing information loss across scales. We introduce **SKILD**, a **S**cale-invariant **K**-Space **I**mage **L**earning **D**iffusion model that unifies generation and continuous super-resolution within a single unconditional framework. Both natural images and critical physical systems exhibit scale invariance, and we leverage it to design a forward process that attenuates image content from fine to coarse scales while injecting spectrum-matched Gaussian noise, making scale an explicit coordinate of the diffusion dynamics. The same trained reverse process performs generation and continuous super-resolution by varying only the starting timestep: *no task-specific architecture, no conditioning branch, no classifier-free guidance, no retraining per scale factor*. Empirically, SKILD reaches FID $2.65$ and Inception Score $9.63$ on unconditional CIFAR-10, performs $2\times$–$8\times$ super-resolution on ImageNet from a single unconditional checkpoint while outperforming conditional models across perceptual metrics, and reconstructs critical Ising models whose connected four-point correlations closely track the ground truth.

Generative modeling for science is data-scarce: each sample is expensive but arrives with several coupled measurements of the same system. Standard conditional models often use single-direction generation, discarding supervision from the rest. We argue that this asymmetry should not be a constraint during training: Under random target/conditioning masking, every measurement can act as a training target, hence one expensive sample yields multiple supervised denoising tasks. We present MOSAIC, a multimodal flow matching transformer over molecular 3D structure and UV, IR, and Raman spectra. On QM9S, MOSAIC achieves state-of-the-art performance with 88.8% Acc@1 for spectra-to-structure task, outperforming the strongest baseline by over 20% with 20$\times$ fewer integration steps. MOSAIC enables any-to-any generation from any combination of input to any combination of output modalities common in chemical sciences. We provide ablations to demonstrate the effectiveness of MOSAIC and show consistent performance scaling with increasing model and data size.