NeurIPS 2026 论文列表

Modern agentic systems extend language models with external tools, enabling them to complete complex tasks through multi-turn interactions. As these agents are deployed in consequential settings, their behavior is often constrained by policies governing pricing, privacy, confirmation, safety, and other operational requirements. However, existing evaluation methods leave a critical gap: capability benchmarks measure what agents can accomplish, while content-safety red-teaming measures harmful generations, but neither tests whether agents comply with predefined policies during otherwise valid tool use. Such policy violations are difficult to audit because their effects may emerge only after several turns, span multiple tool calls, or surface in downstream systems. We propose PROBE (Policy Rule Observation via Behavioral Exploration), an algorithm for training LLM-based auditors that interact with deployed target agents as realistic users and probe them for policy violations through multi-turn, tool-mediated dialogue. We formalize agent auditing as a two-player Markov game and propose a composite reward that balances violation discovery, behavioral plausibility, conversation progress, and policy diversity. To study the role of training objectives and reward design, we compare GRPO, DPO, and GFlowNet-based optimization in a controlled setting. Across extensive experiments, auditors trained with PROBE uncover substantially broader and higher-yield policy violations than same-architecture zero-shot adversaries, even when the auditor is significantly smaller than the target agent. Our results show that multi-turn behavioral auditing is a distinct and necessary axis of agent evaluation, complementing capability and content-safety benchmarks. More broadly, PROBE offers a scalable path toward automated policy auditing for real-world agentic systems and suggests a future co-evolutionary loop in which auditors expose failures and agents improve from auditor feedback.

The transition from Large Language Models (LLMs) to agents shifts safety stakes from toxic text to irreversible environmental harm. While current defenses remain largely retrospective, proactive runtime intervention is bottlenecked by the lack of large-scale, causally-consistent data. We propose PROACT-Agent, a framework for synthesizing high-fidelity trajectories to enable real-time guardrails. We identify a critical "safety drift" in prior benchmarks, where lenient annotation paradigms fail to enforce temporal consistency. PROACT-Agent addresses this through: (1) Progressive Trajectory Unrolling to reveal risks hidden in long-context interactions; (2) Reasoning-Augmented Causal Rectification to enforce monotonic causal consistency; and (3) Culturally-Aware Data Localization for cross-border robustness. We introduce PROACT-Bench, the largest bilingual safety benchmark to date, featuring 140,000+ trajectories adjudicated with industrial-grade rigor. Experiments show that models trained via our framework achieve superior zero-latency intervention, establishing a new standard for real-time autonomous agent safety.

Text watermarking for large language models (LLMs) enables model owners to verify the origin and protect the intellectual property of AI-generated text. While text watermarking for closed-source LLMs is relatively mature, open-sourcing the LLM introduces unique challenges: existing methods inject the watermark during the model owner's decoding process, but for open-source LLMs, end users control decoding and can arbitrarily remove it at inference time. The watermark must therefore be embedded into the released model weights, so that standard decoding natively produces watermarked text. Recent methods attempt this by distilling from a decoding-based watermarked LLM, but two challenges remain: (i) learnability: the LLM may not perfectly internalize the predefined watermark pattern, producing a generation-detection inconsistency between what the LLM generates and what the detector expects; and (ii) robustness: open-sourcing the model exposes the embedded watermark to weight-level user modifications such as fine-tuning and model merging. A practical open-source LLM watermark must therefore align the LLM's learned watermark with the detection pattern, and remain robust against weight-level user modifications. Guided by these requirements, we propose PRO, a precise and robust open-source LLM watermarking method. PRO jointly trains a watermark policy model with the LLM so that the LLM's learned watermark aligns with the detection pattern, and further incorporates a perturbation-aware regularizer that improves robustness against weight-level modifications. Evaluated on several mainstream open-source LLMs, PRO improves watermark detectability to $0.92$ TPR at $1\%$ FPR while reducing perplexity by $20.5\%$ over the strongest prior method, and preserves detectability under aggressive weight-level modifications, e.g., AUC $0.87$ vs. $0.79$ under $0.5$-ratio model merging. The code is publicly available at https://anonymous.4open.science/r/PRO-44B7.

Large language models (LLMs) have recently improved sequential recommendation, yet the task remains a retrieval problem without explicit user queries: the system must infer the next item from user histories where diverse intents and preferences are intertwined. Existing methods typically compress this heterogeneous evidence into a single embedding or query, while LLM-based recommenders can produce overly general queries that are semantically plausible but weakly aligned with retrieval. We propose PRISMIC (Preference Reconstruction via Intent Synthesis and Multi-signal Inference Consolidation), which reformulates sequential recommendation as multi-signal intent consolidation. PRISMIC first generates multiple candidate queries from different historical signals, then trains an LLM-based consolidator with GRPO using an NDCG-based retrieval reward to merge relevant signals into a single query. The resulting consolidated query is not directly used at inference time; instead, it serves as a semantic supervision target for training a user encoder, enabling LLM-free inference. Across four real-world datasets, PRISMIC consistently outperforms strong baselines. Ablations further show that the gains come not from GRPO alone, but from consolidating multiple intent-signals and distilling the consolidated intent into an encoder.

Preference ranking modeling is important for selecting and adapting language-model responses to human preferences. Compared with deterministic ranking methods, generative preference ranking models the prompt-response data distribution, providing richer uncertainty characterization and sample diversity for learning a more expressive ranking boundary. Recent work improves modeling efficiency by shifting generative preference ranking from textual space to embedding space, but without explicit preference-conditioned separation, the learned embedding distribution may still entangle preferred and rejected responses, producing ambiguous synthetic samples that weaken the ranking boundary. To address this issue, we propose PRISM, a Preference Ranking framework through dISentangled embedding Modeling. PRISM formulates embedding-level generative preference ranking with a unified class-conditional ELBO and decomposes this optimization objective to expose the encoded latent entanglement between preferred and rejected embeddings. This derivation motivates two practical preference-aware generation variants: PRISMMI, involving a deep class-separation objective, and PRISMMMD, catering a probabilistic aggregate-matching objective. Both variants learn a preference-disentangled embedding data distribution and synthesize data pairs that preserve ranking semantics for the generative ranking boundary. Experiments show that PRISM improves preference ranking performance and produces useful generated embeddings for downstream response selection. The anonymous code repository is available at:https://anonymous.4open.science/r/PRISM-915C/README.md.

Parameter-Efficient Fine-Tuning (PEFT) has become a dominant paradigm for adapting Multimodal Large Language Models (MLLMs) to specific tasks, resulting in a proliferation of task-specific expert models. Merging these experts into one universal model offers a promising path to creating versatile systems without costly retraining. However, existing methods for full fine-tuning merging often falter in parameter-efficient model merging, as they manipulate weights directly while ignoring the intrinsic geometric structure of PEFT modules. When diverse tasks exhibit conflicting feature directions, this inevitably leads to destructive interference. To address this issue, we propose PRISM, a data-free framework that reframes the merging of PEFT modules as a spectral signal reconstruction problem. PRISM operates in two stages: (1) Spectral Pruning, which decouples task updates into singular components and retains only high-energy directions to attenuate task-irrelevant noise; and (2) Energy-Prioritized Orthogonal Reconstruction, which prioritizes dominant components and projects overlapping vectors onto orthogonal directions to eliminate inter-task interference. We curate a benchmark comprising diverse multimodal tasks to evaluate our method. Extensive experiments demonstrate that PRISM significantly outperforms state-of-the-art baselines. Our code and weights will be released.

Real-world image dehazing (RID) aims to remove haze-induced degradation from real scenes. This task remains challenging due to non-uniform haze distribution, spatially varying color shifts, and the scarcity of paired real hazy-clean data. In PRISM, we propose Proximal Scattering Atmosphere Reconstruction (PSAR), a physically structured framework that jointly reconstructs the clear scene and scattering variables under the atmospheric scattering model, making the restoration process more interpretable in complex real-world conditions. To bridge the synthetic-to-real gap, we design an online non-uniform haze synthesis pipeline and a Selective Self-Distillation Adaptation (SSDA) scheme for unpaired real-world scenarios, which enables the model to selectively learn from high-quality perceptual targets while leveraging its intrinsic scattering understanding to audit residual haze and guide self-refinement. Experiments on real-world benchmarks demonstrate that PRISM achieves competitive performance on RID tasks.

The advancement of Embodied AI necessitates high-quality simulation assets that faithfully mirror the physical world. However, transforming raw visual observations into simulation-ready scenes remains challenging due to the lack of physical grounding and scene-level interactivity in current image-to-URDF methods. We propose **PRISM**, a framework that reformulates monocular scene reconstruction as a procedural programming task for interactive 3D environments. By leveraging the zero-shot reasoning and code synthesis of MLLMs, PRISM translates a single RGB image into executable programs defining object geometry, articulation, and physical properties. To ensure simulation readiness, it incorporates a **physics-in-the-loop** mechanism that iteratively refines the generated programs by validating their execution in a physics engine. This feedback loop enforces physically plausible object articulations, valid object compositions and interactions, and accurate spatial relationships. Experiments demonstrate that PRISM significantly outperforms open-loop approaches and prior monocular reconstruction models. Notably, PRISM-generated scenes support complex downstream tasks such as stable stacking and fine-grained manipulation, which are difficult to achieve with existing methods.

Sparse object detection remains difficult when weak target evidence occupies only a few pixels and shares local statistics with surrounding clutter. This failure is not merely due to limited model capacity, but to a representation bottleneck: spatial domain features mix target edges, clutter textures, and scene context before they can be reliably separated. We propose PRISM, a wavelet domain detector that treats this problem as frequency separated representation learning followed by priority ordered feature interaction. PRISM decomposes each scene into directional high frequency evidence and global low frequency context, models the two streams with dedicated encoders, and fuses them through a learned content adaptive causal scan that lets likely target regions guide feature interaction before clutter dominated regions participate. Multi scale features are then reconstructed by inverse wavelet synthesis with adaptive gates and passed to a standard detection head. On low altitude UAV maritime benchmarks, including SeaDronesSee and AFO, PRISM achieves state-of-the-art performance with the largest gains on the smallest targets. Without architectural modification, it also transfers to aerial urban imagery on VisDrone, suggesting that priority ordered fusion of directional frequency evidence is a generalizable design principle for sparse visual signal detection.

Solubility depends on intermolecular interactions between solutes and solvents and is central to drug discovery, chemical and materials process engineering. Its prediction requires out-of-distribution (OOD) generalization to unseen solutes, solvents, and solvent mixtures, where supervised methods suffer from data scarcity, and existing self-supervised pretraining methods do not explicitly learn chemically meaningful relations. Learning chemically meaningful relational information without labels can alleviate data scarcity and improve OOD generalizations, but remains challenging. We introduce Prior Relational Information for Self-supervised Modeling (PRISM) and apply it to solubility OOD generalizations. PRISM is a label-free pretraining framework that converts expert-defined chemical priors into relational teachers. For solubility prediction, it trains four encoders to preserve the population-level relational information from priors capturing hydrophobic, electrostatic, polarizability, and substructural effects, using Kullback-Leibler (KL) divergence to match pairwise distance-based distributions during pretraining. For scaffold, solute, and solvent OOD generalizations, PRISM matches or surpasses supervised and self-supervised baselines. For mixed-solvent OOD generalizations, freezing the four PRISM encoders and training a light fusion-and-composition head outperforms the strongest supervised baselines and greatly reduces prediction variations. Ablations and control studies confirm that gains stem from chemically meaningful relational structures baked into the encoders by PRISM. We expect this strategy of turning relational priors into self-supervision signals to extend beyond solubility prediction to domains where chemically or biologically meaningful relational priors are available.

Text image super-resolution (Text-SR) requires more than visually plausible detail synthesis: slight errors in stroke topology may alter character identity and break readability. Existing methods improve text fidelity with stronger recognitive or generative priors, yet they still face two unresolved challenges under severe degradation: the text condition extracted from low-quality inputs can itself be unreliable, and a plausible global prior does not fully determine fine-grained stroke boundaries. We present PRISM, a single-step diffusion-based Text-SR framework that addresses these two challenges through Flow-Matching Prior Rectification (FMPR) and a Structure-guided Uncertainty-aware Residual Encoder (SURE). FMPR constructs a privileged training-time prior from paired low-quality/high-quality latents and learns a flow matching that transports degraded embeddings toward this restoration-oriented prior space, yielding more accurate and reliable global text guidance. SURE further predicts uncertainty-aware structural residuals to selectively absorb reliable local boundary evidence while suppressing ambiguous stroke cues. Together, these components enable explicit global prior rectification and local structure refinement within a single diffusion restoration pass. Experiments on both synthetic and real-world benchmarks show that PRISM achieves state-of-the-art performance with millisecond-level inference.

Parameter-efficient fine-tuning (PEFT) methods such as low-rank adaptation have become essential for adapting large language models to downstream tasks. Existing approaches often apply low-rank updates uniformly across all layers, ignoring the structural coupling inherent in transformer architectures. We observe that transformers contain \emph{weight pairs} whose interactions govern model behavior including query-key products, value-output products, and feedforward networks. We propose PRincipal Interaction Subspace Matching (PRISM), a principled framework that exploits this coupling through spectral parameterization. For each weight pair, we parameterize adaptations using the spectral basis of the paired matrix, ensuring that updates occur in the subspace where the paired matrix has maximal effect. We validate our approach through controlled experiments on synthetic models, demonstrating faster convergence. Empirical evaluations on arithmetic reasoning, code generation, and vision tasks show that PRISM outperforms existing PEFT methods.

Vision-Language-Action (VLA) models promise a single language-conditioned policy adaptable to many manipulation behaviors, yet deployment additionally requires skill retention and long-horizon composition: a policy must retain previously acquired skills and reuse them inside composite tasks whose behavior changes across phases. The standard recipe absorbs every new task into one shared update, which provides neither a parameter-level locus for reusable skills nor a mechanism for invoking each skill along the trajectory. We observe that this missing structure is latent rather than absent: per-skill low-rank updates of a pretrained VLA occupy a robustly distinguishable, low-dimensional subspace, and a single composite-task LoRA concentrates roughly half of its Frobenius energy in this subspace, against a near-zero matched random-rotation control. Building on this geometry, we introduce PRISM, which represents reusable skills as parameter-level primitives: each skill family is stored as a dedicated low-rank action-expert adapter in a primitive bank, primitives are composed at inference by a per-chunk VLM-conditioned router that reads the prefix representation to track task progress, and the router is supervised by per-chunk labels from a deterministic, action-derived oracle. During composite training, only the vision encoder, the language LoRA, and the router are updated; every primitive is held frozen, so the per-skill action-expert parameters are preserved by construction. On RoboCasa365, PRISM preserves atomic-skill success through composite training and reaches 20% on long-horizon composite tasks, while a standard $\pi_{0.5}$-LoRA two-stage fine-tune instead loses roughly 25 atomic points and reaches only 11% composite. We additionally validate PRISM on an AgileX PIPER real-robot setup.

RGB cameras struggle on the road surface itself: pavement is nearly texture-less at the centimetre scale, and dry, wet, and icy asphalt can look photometrically identical. Polarization is the natural complement, since Fresnel optics ties the angle of linear polarization (AoLP) to surface-normal azimuth and the degree of linear polarization (DoLP) to refractive index, but no public dataset has made it possible to test whether this physical promise translates into measurable gains on real roads. We introduce PRISM, a polarimetric road-surface dataset of 47,098 time-synchronized frames combining trichromatic linear polarization, co-boresighted RGB, 128-channel LiDAR, and RTK-GNSS/INS, collected across proving-ground and open-road environments under clear, overcast, rainy, foggy, and snowy conditions. Dense road-surface elevation ground truth, validated against as-designed proving-ground geometry, accompanies frame-level labels over five materials and five surface states. Two benchmark tracks are defined on these data: road-surface condition classification and bird's-eye-view elevation estimation. Both share a five-variant input ablation crossing RGB, monochromatic and trichromatic polarization, and their combinations. Reference baselines turn the physical promise into a measurable one. Adding trichromatic polarization to RGB reduces elevation MAE from 3.61 to 3.26cm and improves every threshold metric, while yielding consistent gains on condition classification across backbones. The improvement is task-conditional: trichromatic resolution drives the elevation gain, where AoLP encodes geometric information that intensity cannot, and stratified evaluation localizes where polarimetric channels matter most. PRISM, together with reference baselines, evaluation and stratification scripts, and a datasheet, is publicly available at \url{https://huggingface.co/datasets/NeurIPS-2026-PRISM/PRISM-Dataset}.

Standard single-cell differential expression (DE) analysis identifies genes that change across conditions, but it usually overlooks two key sources of heterogeneity that single-cell data are uniquely positioned to reveal: which cells within a donor are truly disease-affected, and how disease effects depend on cell state or subject-level context. Assigning every cell the diagnosis of its donor can contaminate DE signal when disease penetrance is partial, while modeling each gene with a single disease effect cannot capture heterogeneous responses across cell populations. We present PRISM (Phenotype-Resolved Inference in Single-cell Mixed models), a negative-binomial mixed-effects framework that augments standard DE analysis with three outputs: a context-DE vector $\theta_g$ that tests whether disease effects vary along a biological axis $z_{ij}$ (such as cell state, sex, or age); a cell-level disease posterior $q_{ij}$ that provides unsupervised disease annotation; and a subject-level disease burden $\rho_i$. Naive likelihood-only inference of $q_{ij}$ is not identified onto the disease axis when nuisance variation (batch, cell-cycle, dominant subtypes) competes with disease; we resolve this with a closed-form 1-D Wasserstein-2 projection of the disease-arm posterior onto a bimodal reference marginal that enforces the \{healthy, affected\} structure implied by the disease label.

Multimodal large language models (MLLMs) have achieved remarkable progress on vision–language tasks, yet their reasoning processes remain sometimes unreliable. We introduce PRISM-Bench, short for Puzzle Reasoning with In-Sequence Mistakes, a benchmark of puzzle-based visual challenges designed to evaluate not only whether models can solve problems, but how their reasoning unfolds. Unlike prior evaluations that measure only final-answer accuracy, PRISM-Bench introduces a diagnostic task: given a visual puzzle and a step-by-step chain-of-thought (CoT) containing exactly one error, models must identify the first incorrect step. This setting enables fine-grained assessment of logical consistency, error detection, and visual reasoning. The puzzles in PRISM-Bench require multi-step symbolic, geometric, and analogical reasoning, resisting shortcuts based on superficial pattern matching. Evaluations across state-of-the-art MLLMs reveal a persistent gap between fluent generation and faithful reasoning: models that produce plausible CoTs often fail to locate simple logical faults. By disentangling answer generation from reasoning verification, PRISM-Bench offers a sharper lens on multimodal reasoning competence and underscores the need for diagnostic evaluation protocols in the development of trustworthy MLLMs.

Learning dynamics from sparse partial observations presents a fundamental dilemma: the pointwise mean squared error (MSE) training objective may induce local contraction and empirically lead to topological collapse, whereas matching the system's invariant measure preserves global geometry but lacks temporal ordering constraints. To resolve this, we introduce the $\textbf{P}$oincaré $\textbf{R}$eturn $\textbf{I}$nduced $\textbf{M}$easur$\textbf{e}$ $(\textbf{PRIME})$ framework. PRIME bridges this gap by adopting a novel perspective of recurrence-level temporal organization, motivated by the return map and roof time structure of suspension flow representations. Instead of relying on unstable pointwise matching, PRIME refines the learned dynamics by leveraging recurrence-level temporal information, specifically by aligning the empirical graph measure of the induced return map over recurrence blocks. We evaluate our framework on the partially observed Lorenz63 system, sensor-observed Kuramoto-Sivashinsky equation, and flow past a cylinder. PRIME effectively reduces topological collapse across our benchmarks, improving temporal consistency while maintaining or enhancing long-term geometric and spectral fidelity.

Root cause analysis (RCA) in complex systems is challenging due to error propagation across multiple variables, the need for structural causal knowledge, and the computational cost of inference at test time. We introduce PRIM (Prior-fitted Root cause Identification with Meta-learning), a causal meta-learning approach that frames RCA as a Bayesian inference task over a synthetic prior of causal models. By marginalising out structural uncertainty, PRIM implicitly identifies changes in the data-generating mechanism between baseline and anomalous periods. In doing so, PRIM infers distributional differences without explicit statistical testing, and implicitly learns causal structure without model fitting at test time. Following the simulation-based meta-learning paradigm of prior-fitted networks, PRIM uses a Model-Averaged Causal Estimation (MACE) transformer neural process that jointly attends over observational and anomalous samples and the causal structure of nodes, enabling zero-shot inference in 17\,ms for systems with up to 100 variables. Across synthetic benchmarks and two realistic benchmark datasets, PetShop and CausRCA, PRIM is competitive with methods that are aware of the system's causal graphical structure a priori while outperforming graph-unaware methods on several tasks. Lightweight fine-tuning to specific domains and data dynamics improves performance further.

Agent memory is typically constructed either offline from curated demonstrations or online from post-deployment interactions. However, regardless of how it is built, an agent faces a cold-start gap when first introduced to a new environment without any task-specific experience available. In this paper, we study pre-task memory construction: whether an agent can build procedural memory before observing any target-environment tasks, using only self-generated synthetic practice. Yet, synthetic interaction alone is insufficient, as without controlling what to practice and what to store, synthetic tasks become redundant, infeasible, and ultimately uninformative, and memory further degrades quickly due to unfiltered trajectories. To overcome this, we present Preping, a proposer-guided memory construction framework. At its core is proposer memory, a structured control state that shapes future practice. A Proposer generates synthetic tasks conditioned on this state, a Solver executes them, and a Validator determines which trajectories are eligible for memory insertion while also providing feedback to guide future proposals. Experiments on AppWorld, BFCL v3, and MCP-Universe show that Preping substantially improves over a no-memory baseline and achieves performance competitive with strong playbook-based methods built from offline or online experience, with deployment cost $2.99\times$ lower on AppWorld and $2.23\times$ lower on BFCL v3 than online memory construction. Further analyses reveal that the main benefit does not come from synthetic volume alone, but from proposer-side control over feasibility, redundancy, and coverage, combined with selective memory updates.