NeurIPS 2026 论文列表

Biomicroscopy imaging involves the complex coordination of multiple procedures, such as focusing, illumination, and staining. Traditional unitary methods struggle with dynamical environments, while hybrid models pre-trained on natural images tend to introduce artifacts for higher perceptual metrics. Notably, biological imaging is not a one-off task. It is an iterative process similar to human expert’s observation, verification and adjustment. They can benefit from history, remembering which processing strategies are suitable and can also identify issues such as artifacts and mis-staining from verification. To this end, we propose BioMicroAgents, the first multi-agent framework with co-evolutionary capabilities designed to simulate and optimize whole biomicro imaging process including brightening, denoising, deblur, super-resolution, and virtual staining. Through reinforcement learning, the model can reflect based on historical experience to reduce artifacts and mis-staining, while enhancing edge and structural features to improve the accuracy of downstream tasks. Specifically, BioMicroAgents includes: (i) Multi-modal Analytical Agents, which utilize multimodal RAG to provide template images and biomicro knowledge, and employ the memory mechanism to analyze optimal processing paths; (ii) We propose three biomicro fidelity metrics and utilize three agent groups (MicroNavigator, MicroProcessor, and MicroAuthenticator) to perform rigorous analysis, processing, and verification. (iii) Co-evolutionary ability, which achieves dynamic policy and self-correction through interactive feedback strategies and memory mechanisms, allowing the agent to evolve from history and other agents. We test model across 12 biomicro tasks, BioMicroAgents achieved improvements in perceptual quality while adhering to strict fidelity in sub-cellular features. It also enhances the accuracy of downstream tasks such as segmentation and cell counting, showcasing the potential of general-purpose visual agents in bioscience.

Understanding long-form video remains a fundamental challenge for multimodal large language models (MLLMs). Sparse frame sampling fails to capture fine-grained visual details, while dense sampling quickly exceeds context length limits. Retrieval-augmented generation (RAG) offers a promising middle ground by selectively retrieving relevant video segments for grounded generation, yet its effectiveness critically depends on the quality of the video segments used as retrieval units. In this paper, we investigate RAG for movie understanding, which demands story-level reasoning over characters, events, and narrative arcs spanning hours of content. Scene segmentation, a long-studied problem that partitions movies into semantically coherent units, is a natural candidate for defining such retrieval units. We reexamine whether existing methods actually serve this role through comprehensive evaluation on downstream movie understanding tasks, and find that they consistently fail to outperform naive uniform temporal chunking. Our audit of the most standard scene segmentation benchmarks reveals why: current annotations prioritize visually salient transitions over narrative event structure. Motivated by this mismatch, we introduce NarraScene, a narrative-centric scene segmentation dataset annotated with a three-level cognitive taxonomy spanning physical, character, and narrative change, where every valid boundary requires a narrative-level shift. When used as retrieval units, these narrative-grounded segments outperform uniform chunking on downstream movie understanding tasks, suggesting that the central challenge for scene segmentation in movie RAG is not detecting boundaries, but identifying the narrative event units that matter for movie understanding.

Existing hallucination detectors for retrieval-augmented generation (RAG), whether based on model outputs (e.g., likelihood) or internal representations, typically apply a uniform detection strategy across responses. However, we find that different detection signals are informative in different response regimes, making a uniform strategy insufficient for both short- and long-form generation. In short-form tasks such as factoid QA, hallucination typically appears as selecting an incorrect final answer among multiple context-relevant candidates. In this regime, model-output-based signals that discriminate among plausible answers are particularly important. We therefore propose a bidirectional likelihood measure that evaluates logical consistency based on the retrieved evidence and a reasoning step generated after the response. In contrast, long-form descriptive responses are more prone to gradual drift: as generation proceeds, the model increasingly relies on its own prior generations and parametric knowledge, while unsupported continuations may remain locally plausible, making output-based cues less informative. For this regime, we measure context--knowledge conflict by tracking the directional alignment of context-grounding versus internal-knowledge contributions in the hidden states. Building on these regime-specific signals, we introduce ARGUS, an adaptive hallucination detector that emphasizes the more effective signal according to the response regime. Experiments on multiple RAG benchmarks show that our method achieves strong performance across both short- and long-form settings.

Modern retrieval-augmented systems typically access a corpus through a fixed similarity interface (either sparse or dense) that returns only the top-$k$ most similar documents for a query. While efficient, this design becomes a bottleneck for agentic search: exact lexical matching, conjunctions of weak clues, local context verification, and multi-step hypothesis refinement are difficult to realize with a conventional off-the-shelf retriever, and evidence filtered out early cannot be recovered downstream. This problem becomes particularly acute when agents must iteratively discover intermediate entities, combine partial evidence, and revise their search strategy. To address this problem, we study direct corpus interaction (DCI), a simple alternative in which an agent searches the raw corpus directly using general-purpose terminal tools (e.g., grep, file reads, shell commands, and lightweight scripts), without relying on embedding models, vector indexes, or retrieval APIs. This approach requires no offline indexing and adapts naturally to evolving local corpora. Across benchmarks for information retrieval (i.e., BRIGHT and BEIR) and end-to-end agentic search (i.e., BrowseComp-Plus), this simple setup substantially outperforms strong sparse, dense, and reranking baselines. It also attains strong accuracy on multi-hop question answering without relying on any conventional semantic retriever. Our results suggest that as language agents grow more capable, retrieval quality depends not only on reasoning ability, but also on how richly the corpus interface supports interaction. DCI therefore points to a broader interface design space for agentic search. Our code is available at https://anonymous.4open.science/r/dci-agent/.

Retrieval-augmented generation (RAG) improves the factuality of large language models (LLMs) and vision-language models (VLMs) by grounding generation in external knowledge. However, most existing RAG frameworks assume a centralized retrieval document pool, which is often impractical in sensitive domains such as healthcare, where data are inherently distributed and subject to strict privacy constraints. Recent efforts on decentralized RAG primarily follow prompt-based paradigms that exchange raw, human-readable retrieved content, leading to substantial communication overhead and potential privacy risks. To address these limitations, we propose Representation-based Federated RAG (FedRepRAG), a decentralized RAG framework that avoids raw-data exchange during retrieval. FedRepRAG retrieves information from private local document stores and exchanges only compressed latent representations. To integrate the retrieved knowledge, we introduce a lightweight collaboratively trained projector that maps retrieval embeddings into the feature space of a frozen LLM/VLM backbone. This design makes the exchanged information interpretable only within the federated system, thereby enhancing privacy while substantially reducing communication cost. Experiments on decentralized visual question answering (VQA) and question answering (QA) benchmarks show that FedRepRAG consistently outperforms direct inference and local retrieval baselines, while substantially reducing retrieval tokens, computational cost, and inference-time communication overhead compared with raw-context transfer. Overall, FedRepRAG establishes an effective, efficient, and privacy-preserving framework for federated RAG.

Standard Retrieval Augmented Generation (RAG) is poorly matched to agent memory. Unlike large heterogeneous corpora, agent memory forms a bounded and coherent interaction stream in which many spans are highly correlated or near duplicates. As a result, flat top-$k$ similarity retrieval often returns redundant context, while summary-centric hierarchies can blur the subtle details that distinguish one candidate from another. We argue that agent memory should follow the principle of decoupling before aggregation: the system should first isolate reusable facts, updates, and distinguishing details from similar histories, and only then organise them for efficient retrieval. Based on this principle, we propose xMemory, which constructs a revisable hierarchical memory structure from original messages to segments, memory components, and groups. xMemory segments interaction history into local events, decouples each segment into memory components, aggregates related components into high-level groups using a sparsity--semantic faithfulness objective, and maintains this structure incrementally as memory evolves. At inference time, xMemory retrieves top-down, first selecting a compact backbone of complementary groups and components, and then expanding to segments and raw messages only when additional evidence reduces the reader's uncertainty. Experiments on LoCoMo and PerLTQA across diverse open source and closed source LLMs show consistent gains in answer quality and inference token efficiency, supported by analyses of redundancy, evidence density, and coverage.

Autoregressive graph generation is commonly formulated by linearizing a graph into a sequence via a flat walk over its nodes and edges. While effective for small molecular graphs, such representations do not scale gracefully to large, structurally complex biological graphs, where higher-order organization is both dense and functionally important. To address this limitation, we introduce a hierarchical graph abstraction together with a coarse-to-fine autoregressive pipeline that separates coarsening, tokenization, and decoding, and we study sequence constructions that expose hierarchy in the tokenization. Concretely, we compare a flat representation with two hierarchical alternatives: one that explicitly factorizes communities and their interactions, and another that induces hierarchy through a depth-first traversal. Across eight benchmarks, differences are modest on simpler domains but become pronounced on larger, more structurally complex settings such as cyclic peptides, proteins, and hard conditional-generation tasks. In these regimes, flat representations degrade in validity or distributional fidelity, while the traversal-based hierarchical representation is consistently more reliable; the explicitly factorized variant is generally weaker, suggesting that how hierarchy is exposed in the sequence matters. Finally, the same coarse-to-fine representation naturally enables motif-conditioned generation without architectural changes: on larger and more structurally complex graphs, conditioning on higher-level prefixes yields strong motif retention together with high validity, uniqueness, and novelty, whereas flat representations can exhibit severe mode collapse under comparable constraints.

Outliers are essential for evaluating and improving the robustness of machine learning systems, especially when future distributions may differ significantly from historical training data. In high-stakes applications, robustness often depends on rare cases that finite datasets fail to capture, making simple resampling or perturbation insufficient for stress scenario generation. Existing outlier synthesis methods typically rely on sparse neighborhoods, low support latent regions, or classifier boundary crossings, which can be heuristic, unstable, and tied to specific modalities or architectures. We therefore propose Sinkhorn Boundary Outlier Generation (SBOG), a structured framework for latent-space outlier generation that couples Sinkhorn optimal transport geometry with distributionally robust boundary modeling. The resulting Sinkhorn-induced support cost guides the sampler toward weakly supported boundary regions, while semantic constraints prevent uncontrolled drift from the intended context, yielding controlled deviations from the in-distribution reference measure rather than arbitrary sparse-region samples. Experiments on time series anomaly generation and image outlier synthesis show that our framework produces informative, semantically controlled outliers and improves downstream robustness evaluation across modalities, providing a foundation for stress scenario generation beyond empirical support.

Robust optimization (RO) provides a principled framework for decision-making under uncertainty, but its practical use is often limited by the need to manually reformulate uncertain optimization models into tractable deterministic counterparts. Recent large language models (LLMs) have been shown promising for automating optimization formulation, yet RO reformulation remains challenging because it requires precise multi-step reasoning and mathematically consistent transformations. To facilitate systematic evaluation of LLM-based reformulation, for which no dedicated benchmark currently exists, we develop AutoRO-Bench, a benchmark featuring an automated data generation pipeline for the core RO reformulation task and a curated dataset for the RO application task. To address the reformulation challenge, we propose Automated Reformulation with Experience Memory (AutoREM), a tuning-free memory-augmented framework that autonomously builds a structured textual experience memory by reflecting on past failed trajectories through a tailored offline adaptation procedure. AutoREM requires neither domain-specific expert knowledge nor parameter updates, and the resulting memory readily transfers across different base LLMs. Experimental results show that AutoREM consistently improves the accuracy and efficiency of RO reformulation across in-distribution datasets, out-of-distribution datasets, and diverse base LLMs.

Language agents increasingly operate over streams of related tasks, yet existing memory systems struggle to convert accumulated experience into reusable knowledge. Retrieval-augmented and structured memory methods record per-session observations effectively, but often couple acquisition and consolidation into a single online process, leaving the agent without a global view across sessions to discover recurring patterns, abstract shared procedures, or prune redundant entries. Inspired by complementary learning systems theory, we propose Auto-Dreamer, a learned offline consolidator for language-agent memory. Auto-Dreamer decouples fast per-session memory acquisition from slow cross-session consolidation. Given a region of a typed memory bank, the consolidator performs bounded tool-use to search memory, inspect candidate entries, and trace them back to raw source trajectories, synthesizing provenance-grounded replacement memories that supersede the original region. We train Auto-Dreamer via GRPO, using end-to-end agent performance as the reward signal to learn how to consolidate memories acquired through fast online experience. Trained on ScienceWorld trajectories alone, Auto-Dreamer transfers without retraining to held-out ALFWorld and WebArena, improves task success over fixed, RL-trained, and prompted memory baselines in both continual-memory deployment and fixed-bank consolidation, and does so with an active memory bank an order of magnitude smaller than competitive baselines.

Predictive coding (PC) is a local-learning alternative to backpropagation (BP), training deep networks via a local energy-minimization dynamics rather than a global backward pass. We introduce Augmented-Lagrangian Predictive Coding (PC-ALM), which maintains PC's inference budget but aligns each weight update toward BP by accumulating per-layer constraint errors into a layer-local Lagrange multiplier. In linear PC networks, PC-ALM converges to an equilibrium with exact BP gradients distributed across the network via only layer-local updates. We analyze PC-ALM in nonlinear PC networks up to depth 128 and show that it matches BP performance across all width-depth regimes, notably in deep narrow networks where PC underperforms. PC-ALM introduces recurrent dynamics in each layer's activations. Compared to PC's heat flow on a scalar energy, PC-ALM dynamics are driven by dual ascent on the Augmented Lagrangian. We observe ballistic credit propagation across especially deep networks, with prediction errors evenly distributed across layers, compared to PC's diffusive credit propagation. Beyond the algorithm itself, the augmented Lagrangian framework offers a generalization of PC, and may yield insights on how distributed systems could compute and propagate BP-like credit signals through purely local dynamics.

We study policy optimization for infinite-horizon, discounted constrained Markov decision processes (CMDPs). While existing theoretical guarantees typically hold for the mixture policy, deploying such a policy is computationally and memory intensive. This leads to a practical mismatch where a single (last-iterate) policy must be deployed. Recent theoretical works have thus focused on proving last-iterate convergence, but are largely limited to the tabular setting or to algorithmic variants that are rarely used in practice. To address this, we use the classic inexact augmented Lagrangian ($\texttt{AL}$) method from constrained optimization, and propose a general framework with provable last-iterate convergence for CMDPs. We first focus on the tabular setting and propose to solve the $\texttt{AL}$ sub-problem with projected Q-ascent ($\texttt{PQA}$). Combining the theoretical guarantees of $\texttt{PQA}$ and the standard $\texttt{AL}$ analysis enables us to establish global last-iterate convergence. We generalize these results to handle log-linear policies, and demonstrate that an efficient, projected variant of $\texttt{PQA}$ can achieve last-iterate convergence with comparable guarantees as prior work. Finally, we demonstrate that our framework scales to complex non-linear policies, and evaluate it on continuous control tasks.

Anatomical mesh segmentation requires models that operate directly on irregular surface geometry while remaining robust to arbitrary patient pose and mesh resolution variation. Existing task-specific mesh and point-cloud methods are not equivariant, and can degrade sharply under test-time perturbation, for example dropping by 25-26 IoU points on intraoral scan segmentation at 40$\textdegree{}$ tilt. We present EAMS, an Equivariant Anatomical Mesh Segmentor built on Equivariant Mesh Neural Networks (EMNN), and evaluate it across four clinically distinct tasks spanning edge-, vertex-, and face-level supervision. We combine intrinsic mesh descriptors with anatomy-aware priors, including PCA-derived frames for dental arches and liver surfaces, and augment message passing to provide lightweight global context. Across intracranial aneurysm and intraoral segmentation, EAMS variants are competitive with specialized baselines on unperturbed inputs while remaining stable under geometric perturbations, and on liver surfaces they expose a favorable trade-off between canonical-pose accuracy and rotation robustness. These results show that a lightweight ($<2$M parameters) equivariant framework can deliver robust anatomical mesh segmentation across diverse supervision types without task-specific architectures.

Conditional generators provide a natural tool for controllable generation, including settings where the desired condition is a new composition of observed attributes or experimental factors. In many applications, especially in scientific domains, such models are attractive to explore conditions for which real samples are rare, expensive, or not yet observed. However, this creates a circularity for evaluation: standard conditional quality metrics require a reference target distribution, but in the extrapolative regime that distribution is unavailable by definition. We address this problem with a post-hoc, per-sample trust score for assessing conditional samples using only the training distribution. The score combines two estimable quantities: global realism, measuring compatibility with the real data manifold, and attribute-wise faithfulness, measuring whether a sample is closer to the requested attributes than to plausible alternatives. We show that the score can recover meaningful comparisons across extrapolated generations, under a mild coverage condition on the observed attributes. These comparisons enable effective filtering, ranking, and abstention of generations and can be used directly on off-the-shelf pretrained models. In biological imaging, selected samples preserve real morphological structure better and improve downstream predictive performance, while similar gains are observed on controlled vision benchmarks. Finally, we show how the score can be applied during generation, enabling abstention before full decoding.

Large language models are increasingly deployed as LLM-as-a-Service (LLMaaS), where user queries are sent to remote LLMs for inference, raising severe privacy concerns about the exposure of sensitive user inputs. Differentially private inference mitigates these risks by injecting noise into input embeddings. However, existing methods struggle to balance privacy and utility, as stronger privacy protection often leads to substantial accuracy degradation in LLM inference. In this work, we propose DP-SPD, a differentially private LLM inference framework that protects user queries by hiding the real query among client-generated dummy queries. All queries are randomly shuffled before being sent to the server, preventing direct identification of the user's true intent. To improve inference efficiency, we exploit the shared-prefix structure among queries and reuse transformer key--value (KV) caches during decoding. We analyze the privacy guarantee of DP-SPD and evaluate its utility on classification and generation tasks, as well as its privacy protection under EIA, MIA, and GPT-based attacks. Experimental results show that our method achieves strong inference-time privacy protection while maintaining utility close to plain-text inference under practical latency.

Analog circuit design remains highly dependent on expert knowledge due to the complexity of device-level interactions and topology design. Recent transformer-based approaches for device-level topology generation have shown promise, yet they suffer from low electrical validity without human-in-the-loop (HITL) training and severe memorization caused by sequence-based circuit representations. In this work, we propose **AnalogToBi**, a framework for device-level analog circuit topology generation. AnalogToBi introduces *circuit-type conditioning* for categorizing heterogeneous multi-type topology datasets, *device renaming augmentation* to mitigate memorization, *a bipartite graph representation* for improved structural generalization, and *grammar-guided decoding* to enforce structural validity during bipartite graph generation. Experimental results demonstrate that AnalogToBi achieves high validity and novelty without HITL training while effectively avoiding memorization of training topologies.

Retrieval-augmented forecasting promises to adapt frozen Time Series Foundation Models (TSFMs) to new domains without fine-tuning, but recent methods typically rely on learned fusion modules, i.e., trained adapters that merge retrieved examples into the backbone's forecast, based on the assumption that frozen backbones cannot dynamically incorporate retrieved context on their own. We show this assumption is unnecessary. We introduce $\textbf{Align-RAG}$, a training-free method that applies a closed-form per-pair amplitude rescaling and integer-lag phase shift to retrieved past--future windows before they enter a frozen backbone's context. With no learned parameters, Align-RAG outperforms the state-of-the-art trained retrieval adapter on a frozen Chronos-Bolt on all seven datasets of the standard benchmark (avg $-3.75\%$ MSE), showing that the gains previously attributed to learned fusion are recoverable without any training. Align-RAG further improves zero-shot MSE on four additional frozen TSFMs with various architectures by $2.5\%$ to $13.7\%$ per backbone with no per-backbone tuning. To probe why alignment helps, we compare the frozen backbone's prediction shift under aligned demonstrations to the closed-form ridge prediction shift on the same pairs. We find that aligned demonstrations induce prediction shifts that track a closed-form ridge predictor on the same pairs, with a future-shuffle control ruling out a futures-averaging account. Together, these results indicate that frozen TSFMs already support dynamic in-context use of retrievals, and that closed-form alignment should be the default baseline for retrieval-augmented forecasting before any fusion module is trained. Code available at: \url{https://anonymous.4open.science/r/phase-rag-4D92}

The expansion of retrieval-augmented generation (RAG) into multimodal domains has intensified the challenge for processing complex visual documents, such as financial reports. While page-level chunking and retrieval is a natural starting point, it creates a critical bottleneck: delivering entire pages to the generator introduces excessive extraneous context. This not only overloads the generator's attention mechanism but also dilutes the most salient evidence. Moreover, compressing these information-rich pages into a limited visual token budget further increases the risk of hallucinations. To address this, we introduce AgenticOCR, a dynamic parsing approach that extends optical character recognition (OCR) from a static, full-text process into a query-driven, on-demand extraction system. By autonomously analyzing document layout in a "thinking with images" manner, AgenticOCR identifies and selectively recognizes regions of interest. This approach performs on-demand decompression of visual tokens precisely where needed, effectively decoupling retrieval granularity from rigid page-level chunking. AgenticOCR has the potential to serve as the "third building block" of the visual document RAG stack, operating alongside and enhancing standard Embedding and Reranking modules. Experimental results demonstrate that AgenticOCR improves both the accuracy and efficiency of visual RAG systems with pixel-adaptive per-image token allocation (e.g., Qwen3.5 generator), achieving expert-level performance in long document understanding. Our repository is anonymously available at https://anonymous.4open.science/r/AgenticOCR.

Neural subgraph retrieval systems, which retrieve corpus graphs containing a query graph as a subgraph, are used in safety-critical applications such as drug discovery, hardware trojan detection, and molecular similarity search. Despite their importance, the adversarial robustness of these systems remains unstudied. We propose GRAP, the first adversarial attack framework targeting subgraph matching-based graph retrieval. \our jointly selects a budget-constrained subset of corpus graphs and computes per-graph edge perturbations to maximally degrade retrieval quality. We formalize two attack regimes: a ranking attack (with solver access) that corrupts pairwise relevance ordering while preserving relevance labels, and a solver-free top-K attack that directly displaces relevant results from retrieved sets. In both cases, we show that the resulting adversarial set functions are monotone and approximately submodular, enabling greedy subset selection with provable approximation guarantees. Experiments on five benchmark datasets against multiple state-of-the-art victim retrievers demonstrate that our method consistently and significantly outperforms all baselines in both gray-box and black-box settings, with and without solver access.