NeurIPS 2026 论文列表

Ambient intelligence seeks to continuously understand human presence, activity, and physiology in physical spaces, enabling smart environments, health monitoring, and human-computer interaction. WiFi infrastructure offers a ubiquitous, always-on, and privacy-preserving sensing substrate across billions of IoT devices. Yet ambient sensing with WiFi remains largely fragmented, with most systems relying on task-specific models, labeled data collection, and customized training pipelines. We present AmbientFM, the first foundation model for ambient sensing through ubiquitous WiFi signals. AmbientFM is pre-trained on 9.2 million unlabeled Channel State Information (CSI) samples collected over 439 days from 20 commercial device types deployed in real-world environments. It learns transferable wireless representations through contrastive learning, masked reconstruction, and physics-informed objectives tailored to wireless signals. With lightweight adaptation, a single backbone transfers to 9 downstream tasks, achieving above 0.90 AUROC on all classification benchmarks and supporting dense spatial reconstruction. AmbientFM reduces the need for labeled data and task-specific design, enabling scalable ambient intelligence on existing wireless infrastructure.

With the rising need for spatially grounded tasks such as Vision-Language Navigation/Action, allocentric perception capabilities in Vision-Language Models (VLMs) are receiving growing focus. However, VLMs remain brittle on allocentric spatial queries that require explicit perspective shifts, where the answer depends on reasoning in a target-centric frame rather than the observed camera view. Thus, we introduce $\textbf{Allocentric Perceiver}$, a training-free strategy that recovers metric 3D states from one or more images with off-the-shelf geometric experts, and then instantiates a query-conditioned allocentric reference frame aligned with the instruction’s semantic intent. By deterministically transforming reconstructed geometry into the target frame and prompting the backbone VLM with structured, geometry-grounded representations, Allocentric Perceiver offloads mental rotation from implicit reasoning to explicit computation. We evaluate Allocentric Perceiver across multiple backbone families on spatial reasoning benchmarks, observing consistent and substantial gains ($\sim$\textbf{10\%}) on allocentric tasks while maintaining strong egocentric performance, and surpassing both spatial‑perception-finetuned models and state‑of‑the‑art open‑source and proprietary models.

Engineering LLMs to accelerate life sciences research requires a robust alignment with biomedical knowledge. We observe that biomedical text exhibits a fundamentally different uncertainty structure from general text: dense low-confidence runs encode epistemic knowledge gaps (dense causal chains, rare entities) rather than the sparse aleatoric stylistic variation typical of general text. Based on this discovery, we propose Balanced Fine-Tuning (BFT), a dual-scale post-training method that combines group-normalized token reweighting with sequence-level reallocation toward knowledge-dense samples exhibiting dense epistemic uncertainty. Across medical evaluation, biological reasoning, sparse-reward RL, and biological representation tasks, BFT provides more consistent gains than SFT and DFT under a shared training setup. When replacing the default closed-source backbones in GeneAgent (GPT-4o) and VCWorld (Gemini-2.5-Flash), the BFT-aligned 70B model delivers stronger performance across biological process reasoning and chemical perturbation prediction. Critically, all BFT variants further improve after subsequent GRPO with sparse rewards, while SFT and DFT degrade, suggesting that epistemic-aware post-training provides a more robust policy initialization. Beyond text generation, BFT-aligned LLMs produce more accurate and professional biomedical profile texts; after encoding these profiles with a text embedding model, the resulting representations support gene-level, cell-level, and perturbation-response tasks, suggesting that BFT-enhanced generation can facilitate biological representation and, in turn, broader biomedical downstream tasks.

Training large language models (LLMs) as multi-turn conversational agents remains a significant challenge, particularly in goal-oriented settings. The difficulty stems from sparse, long-horizon objectives and the discrepancy between response-level planning and token-level generation. In this paper, we present a formal reduction of the multi-turn RL problem into a \emph{sequence of single-turn RLHF-style problems}. This is achieved by setting a learned multi-turn Q-function as the reward model for the single-turn problem. We demonstrate and prove a key insight: solving this single-turn RLHF problem with standard token-level GRPO is equivalent to an approximate policy improvement step within the multi-turn problem. This insight naturally leads to \emph{Iterative GRPO}, a batch online approximate policy iteration algorithm that alternates between collecting a batch of data from the current policy, fitting Q-functions from these logged conversation trajectories, and improving the policy via single-turn RLHF. A major practical advantage is that Iterative GRPO directly leverages stable, off-the-shelf single-turn RLHF tools, making it straightforward to implement. In addition, the method occupies a middle ground between fully online and fully offline approaches, retaining the adaptability of online updates while gaining the stability benefits of offline training. Empirically, we demonstrate the effectiveness of Iterative GRPO on six multi-turn conversational environments.

Vision-language models such as CLIP often struggle to faithfully understand long, detail-rich captions, relying on dominant scene cues while overlooking fine-grained visual evidence. We propose a hierarchical vision-language learning principle for understanding scenes as part-to-whole compositions: before forming a whole-scene representation, a model should uncover what semantic parts appear where in the image. To this end, we propose CAFT (Cross-domain Alignment of Forests and Trees), a vision-language model that jointly learns local text-region alignment at intermediate representations and global image-text alignment at the final representation. Exploiting the organization of long captions, where local descriptions often correspond to scene parts, CAFT employs a fine-to-coarse image encoder and a part-whole text encoder to discover localized part semantics and progressively compose them into a global image-text representation. Trained on 30M image-text pairs, CAFT achieves state-of-the-art performance on six long-text retrieval benchmarks and exhibits strong scaling behavior. Experiments show that CAFT learns fine-grained representations that localize textual semantics in image regions without explicit region-level supervision.

Contextual queueing bandits provide a framework for learning to schedule heterogeneous jobs under unknown context-dependent service rates. Under stochastic contexts, existing algorithms achieve $\widetilde{\cal O}(T^{-1/4})$ queue length regret, defined as the expected difference between the learner's and oracle's queue lengths at horizon $T$. In this paper, we improve this rate to $\widetilde{\cal O}(T^{-1/2})$. The key observation is that random exploration is needed only up to a carefully chosen cutoff round, rather than throughout the entire horizon. We propose CQB-$\eta$-2, a three-phase algorithm: (i) pure random exploration to construct an initial estimator, (ii) $\eta$-random exploration combined with a UCB rule to continue learning while maintaining negative drift, and (iii) pure UCB after the exploration cutoff. Our proof decomposes the queue length regret at the cutoff round. Before the cutoff, negative drift suppresses queue length differences caused by suboptimal choices. After the cutoff, the first two phases provide sufficient random exploration samples, ensuring that UCB decisions incur small departure-rate gaps. Combining these two bounds yields queue length regret of order $\widetilde{\cal O}(T^{-1/2})$. We further prove a minimax lower bound of order $\Omega(T^{-1/2})$. The proof constructs two hard instances that are statistically indistinguishable up to the final service decision, and uses a queue-specific coupling argument to convert the resulting testing error into queue length regret. Together, our upper and lower bounds characterize the minimax dependence on the horizon $T$ up to logarithmic factors.

Multimodal large language models offer a promising foundation for industrial anomaly detection (IAD), yet they remain unreliable for fine-grained diagnosis, where subtle defects require tight alignment between localized visual evidence and domain-specific semantics. Existing single-pass frameworks produce predictions without revisiting or validating intermediate evidence, which leads to spatio-semantic misalignment and error accumulation. More importantly, they lack mechanisms to actively control how additional evidence is acquired and integrated during reasoning, limiting their ability to resolve ambiguous cases. To address these limitations, we propose AirIAD, an agentic iterative reasoning framework that unifies progressive refinement with active information acquisition. AirIAD equips the model with two tools, Perceptive Zoomer and Knowledge Retriever, which enable it to iteratively gather complementary visual and semantic evidence. Through this process, the model performs explicit cross-verification between what is observed and what is known, refining intermediate hypotheses until convergence on a reliable diagnosis. This capability is enabled by a two-stage training pipeline. A supervised fine-tuning stage introduces a Spatio-Semantic Cross-Verification Chain-of-Thought, which structures coordinated perception and knowledge grounding. This is followed by a Spatio-Semantic Group-in-Group Policy Optimization stage, which provides dense step-wise supervision to encourage consistent self-refinement and robust spatio-semantic alignment. Extensive experiments on the MMAD benchmark show that AirIAD, built on Qwen3-VL-Instruct-4B, achieves state-of-the-art performance in anomaly detection and fine-grained diagnosis.

Modern agricultural data is sourced from diverse platforms and spans multiple spatial scales, ranging from ground-level close-up photography to Unmanned Aerial Vehicle (UAV) aerial observation and satellite remote sensing imagery. Accordingly, agricultural multimodal reasoning demands robust cross-scale spatial understanding. However, due to the lack of multi-view agricultural benchmark datasets, existing multimodal large language models (MLLMs) exhibit severe ground-level bias, which leads to scale confusion then semantic collapse in agricultural perception tasks, such as misinterpreting farmland imagery as walls or floors. To address this, we introduce \textbf{AgroOmni}, a large-scale multi-view training corpus with 288K Visual Question Answering pairs covering 56 specialized task categories across 14 task types, designed to capture diverse scales in modern precision agriculture. Built on this dataset, we propose AgroNVILA, which achieves a new state-of-the-art of 62.32\% on the AgroMind benchmark ($+15.03\%$ over GPT-5.2), effectively mitigating the multi-view cross-scale gap for holistic agricultural understanding. Diagnostic evaluations on AgMMU further reveal an inherent heterogeneity between macro-priors and micro-diagnostics through constrained zero-shot performance. Meanwhile, even minimal fine-tuning leads to a dramatic performance gain of AgroNVILA on AgMMU, strongly demonstrating its generalization capability empowered by AgroOmni. Full training scripts are publicly available at \url{https://anonymous.4open.science/r/AgroOmni-6510}.

Learning high-quality multimodal entity representations is essential for advancing reasoning tasks such as multimodal knowledge graph completion (MMKGC). However, rigid alignment in existing fusion strategies can bias representations toward a dominant anchor modality, weakening fine-grained complementary cues and propagating noisy signals into relational reasoning. To address these limitations, we propose MANoR, a Multimodal Autonomous Negotiation framework for calibrated Representation learning. MANoR treats modalities as autonomous semantic agents that negotiate before fusion. Specifically, MANoR preserves modality-specific semantics through centroid-guided subspace contraction, which reduces unstable centroid-relative dispersion while retaining entity-level residual semantics. On this stabilized basis, negotiated attention enables selective cross-modal interaction without collapsing modality boundaries. As modalities can still differ in reliability after interaction, uncertainty calibrated fusion estimates dimension-level aleatoric uncertainty and modality-level reliability to compute relation-aware fusion weights. In this way, MANoR couples modality autonomy, selective interaction, and calibrated integration within a unified representation learning framework. Experiments on MKG-W, MKG-Y, TIVA, and KVC16K show that MANoR consistently outperforms strong MMKGC baselines, achieving a relative Hits@1 gain of up to 17.72% on KVC16K.

Static bug analyzers play a crucial role in ensuring software quality. However, existing analyzers for bug detection in large-scale codebases often suffer from high false positive rates. This limitation largely stems from their inadequate capabilities in performing precise path feasibility validation for complex code contexts. While recent work has explored Large Language Models (LLMs) to eliminate false positives in static bug detection, the limited reasoning capabilities of LLMs over long and complex contexts hinder their effectiveness when applied to large-scale software projects. To address this challenge, we propose PathAgent, an agent-driven framework for fine-grained path feasibility analysis. PathAgent decomposes complex inter-procedural analysis into localized symbolic range reasoning sub-tasks, and employs an on-demand contextual exploration strategy to adaptively retrieve only the necessary code context. These designs enable precise path feasibility reasoning and effectively reduce false positives reported by static bug analyzers. Evaluation on large-scale real-world projects shows that PathAgent eliminates 79% of false positives, outperforming all baselines by 23% to 44%, while maintaining strong bug detection capability with a recall of 0.94.

AI agent research spans a wide spectrum: from RL agents that learn from scratch to foundation model agents that leverage pre-trained knowledge, yet no unified benchmark enables fair comparison across these approaches. We present **Agentick**, a benchmark for sequential decision-making agents designed to evaluate RL, LLM, VLM, hybrid, and human agents on common ground and to power research on the fundamental challenges of sequential decision-making. Agentick provides 37 procedurally generated tasks across six capability categories, four difficulty levels, and five observation modalities, all exposed through a single Gymnasium-compatible interface. The benchmark ships with a Coding API, oracle reference policies for all tasks, pre-built SFT datasets, a composable agent harness, and a live leaderboard. An evaluation spanning 27 configurations and over 90,000 episodes reveals that no single approach dominates: GPT-5 mini leads overall at 0.309 oracle-normalized score while PPO dominates planning and multi-agent tasks; the reasoning harness multiplies LLM performance by 3--10x; and ASCII observations consistently outperform natural language. These findings highlight the substantial room for improvement that remains across all agent paradigms. Agentick's capability-decomposed, multi-modal design provides the empirical infrastructure needed to drive progress toward general autonomous agents, both as an evaluation framework and as a training ground for RL post-training of foundation models in truly sequential environments.

Video production workflows offer a rich and demanding testbed for evaluating multimodal AI agents: they require composite capabilities across text, image, audio, and video understanding, along with long-horizon planning, and tool use. To this end, we introduce AgenticVBench, a benchmark of 100 tasks across 4 task families spanning the real world post-production workflow, constructed from the representative work of 20 industry experts averaging 6 years of experience across video production contexts. Tasks are paired with evaluation specifications that combine programmatic verifiers and expert rubrics, both authored under expert protocol. We evaluate frontier vision-language models (VLMs) with vendor-native and open-source harnesses. The best evaluated model achieves a score below 30\%, substantially lower than frontier performance on existing multimodal benchmarks. We further find that the choice of harness substantially affects model behavior, including but not limited to scores and failure modes. AgenticVBench provides a foundation for understanding and improving both models and harnesses for agentic video production.

Real-world multimodal agents solve multi-step workflows grounded in visual evidence. For example, an agent can troubleshoot a device by linking a wiring photo to a schematic and validating the fix with online documentation, or plan a trip by interpreting a transit map and checking schedules under routing constraints. However, existing multimodal benchmarks mainly evaluate single-turn visual reasoning or specific tool skills, and they do not fully capture the realism, visual subtlety, and long-horizon tool use that practical agents require. We introduce AgentVista, a benchmark for generalist multimodal agents that spans 25 sub-domains across 7 categories, pairing realistic and detail-rich visual scenarios with natural hybrid tool use. Tasks require long-horizon tool interactions across modalities, including web search, image search, page navigation, and code-based operations for both image processing and general programming. Comprehensive evaluation of state-of-the-art models exposes significant gaps in their ability to carry out long-horizon multimodal tool use. Even the best model in our evaluation, GPT-5.4 with tools, achieves only 31.58% overall accuracy, and hard instances can extend to 25 tool-calling turns. We expect AgentVista to accelerate the development of more capable and reliable multimodal agents for realistic and ultra-challenging problem solving.

Vision-language models with extended reasoning succeed on complex problems, but many real-world problems require external tools that internal reasoning alone cannot reach. Agentic reasoning therefore interleaves two behaviors with a structural asymmetry: thinking (the self-contained default) and tool use (a high-variance auxiliary acting). We refer to this asymmetry as the Thinking-Acting Gap. Under standard RL recipes like GRPO, the gap manifests as two diagnostic symptoms during training: tool use is attempted on only ~30% of rollouts, and when attempted, the tool-using rollouts within a group are all-wrong on ~40% of questions, suppressing the learning signal at the tool calls that needed it. We propose AXPO (Agent eXplorative Policy Optimization): for each all-wrong tool-using subgroup, AXPO fixes the thinking prefix and resamples the tool call and its continuation, paired with uncertainty-based prefix selection. Across nine multimodal benchmarks and three scales of Qwen3-VL-Thinking, SFT+AXPO outperforms SFT+GRPO at average (+1.8pp Pass@1 and +1.8pp Pass@4 at 8B on average) and 8B with SFT+AXPO surpasses the 32B Base on Pass@4 with 4x fewer parameters.

Many everyday robot manipulation skills are affordance-dependent, with success determined by whether the robot contacts the functional object region required by the subsequent action. Current simulation data generators obtain contacts from generic grasp estimators or per-object manual contact annotations, but generic estimators rank stable grasps without task semantics and often select contacts that are misaligned with the downstream action, while manual contact annotations must be rewritten for each new object and task. To solve these challenges, we introduce AffordSim, a scalable data generator and benchmark that integrates open-vocabulary 3D affordance prediction into simulation-based trajectory generation. Given a natural-language task description, AffordSim synthesizes a task-relevant scene, emits affordance queries, grounds them on object surfaces, samples region-conditioned grasps, and selects executable candidates with motion planning. It further randomizes object pose, texture, lighting, image noise, and cross-viewpoint backgrounds for sim-to-real transfer. We instantiate AffordSim as a 50-task benchmark across diverse manipulation skills, five robot embodiments, and 500+ rigid and articulated objects. AffordSim achieves $93\\%$ of the trajectory collection success rate of manual contact annotations on affordance-critical tasks and $89\\%$ on hard composite tasks. Vision-language-action policies trained on AffordSim data transfer zero-shot to a real Franka FR3, reaching $24\\%$ average success. The data generator, benchmark, and code are available at \href{https://anonymous.4open.science/r/AffordSim-8D94/}{project page}.

While bound propagation methods are highly effective for certifying neural network robustness against $\ell_{\infty}$ adversaries, scalable verification for $\ell_{2}$ perturbations remains a significant open challenge. Existing approaches suffer from severe geometric loss: elementwise bounding methods like $\alpha$-CROWN relax Euclidean balls to bounding boxes, losing a factor of $\sqrt{n}$ in radius, while the recently proposed SDP-CROWN rounds deformed ellipsoids back into balls, losing a factor proportional to the condition number of the weights. Consequently, tight $\ell_{2}$ verification is currently restricted to near-isometric or Lipschitz-regularized networks where these approximations are not vacuous. In this paper, we introduce affine-image propagation, a novel CROWN-compatible framework that exactly propagates the affine geometry of $\ell_{2}$ balls and $\ell_{\infty}$ boxes through the network. To bypass the prohibitive computational cost of exact Semidefinite Programming (SDP) constraints required by this representation, we derive highly scalable, SDP-based bounds utilizing diagonal-dominance surrogates. Our approach effectively eliminates the geometric bottlenecks of prior work, yielding the first large-scale, SDP-style bound propagation method capable of tight $\ell_{2}$ verification for general, non-Lipschitz-regularized neural networks.

The rapid advancement of generative models has led to a new era of AI-generated images (AIGIs). Early generation models often suffered from basic defects, such as geometric distortions and poor text-image alignment. Modern generation models have largely overcome these issues, narrowing the perceptual gap between AI-generated and real-world images, thereby shifting evaluation from basic correctness to high-level aesthetic excellence. In this paper, we introduce **AesGI-Bench**, a comprehensive and multidimensional benchmark for fine-grained aesthetic evaluation of AIGIs. Our benchmark covers 18 representative generation models, 19,485 images, and 117K pairwise comparisons from perspectives of visual aesthetic, technical quality, and style alignment. Based on AesGI-Bench, we propose **AesGI-Assessor**, a lightweight and human preference-aligned aesthetic evaluation model for AIGIs. Built upon Qwen3.5, AesGI-Assessor achieves a **pairwise-to-score** learning paradigm, using tie-aware pairwise loss for training and three lightweight score heads for multi-dimensional aesthetic score prediction. Although trained from pairwise human preferences, it learns to predict precise continuous scores for individual images, enabling both fine-grained image-level evaluation and model-level ranking. Furthermore, we explore the potential of large multimodal models (LMMs) as automatic aesthetic evaluators. Experiments show that LMM-based evaluators better align with human aesthetic preferences than conventional metrics. AesGI-Assessor achieves the highest agreement with human judgments, validating its effectiveness as a lightweight, preference-aligned evaluator for fine-grained AIGI aesthetic assessment. The database and codes are publicly available.

Atmospheric pollution risk inference is not merely generic time-series extrapolation: target-day risk depends jointly on numerically preserved pollutant state, exogenous meteorological regime, and directed cross-city transport. Existing time-series models are strong sequence learners, graph-based air-quality models often rely on static or weakly directed spatial priors, and multimodal methods commonly merge heterogeneous evidence without specifying its functional role. We present \ourmodel{}, a transport-aware multimodal evidence-fusion framework that assembles an atmospheric evidence mosaic from numerical pollutant/time states, compact precipitation text, and wind-aligned graph structure. The model preserves historical PM$_{2.5}$ and time features as numerical prefix tokens, compresses recent precipitation into prompt-side context, constructs a daily wind-driven DAG to organize upwind-to-downwind evidence, and refines city embeddings through post-pooling DAG residual propagation with root-sensitive mechanisms. We analyze \ourmodel{} on six PM$_{2.5}$ risk-inference benchmarks under a unified year-based split. The results provide protocol-bounded evidence that transport-aligned structure and role-separated interfaces yield useful risk-inference signals, while leaving no-oracle evaluation as future work.

Autoregressive video generation has improved rapidly in visual fidelity and interactivity, but it still suffers from long-term inconsistency and memory degradation. Most existing solutions either compress historical frames using predefined strategies or retrieve keyframes based on coarse implicit attention signals, both of which fail to handle evolving prompts with shifting entity references, leading to identity drift, character duplication, and attribute loss. To address this, we propose IAMFlow, a training-free identity-aware memory framework that explicitly models and tracks persistent entity identities, enabling consistent generation across prompt transitions. Specifically, an LLM extracts entities with visual attributes from each prompt and assigns unique global IDs for identity-aware memory, while a VLM asynchronously verifies and refines attributes from rendered frames, enabling explicit entity tracking in place of implicit similarity-based matching. To keep the proposed framework computationally practical, we design a systematic inference acceleration pipeline, including asynchronous visual verification, adaptive prompt transition, and model quantization, which achieves faster generation than existing baselines. Furthermore, we introduce NarraStream-Bench, a benchmark for narrative streaming video generation that features 324 multi-prompt scripts spanning six dimensions and a three-dimensional evaluation protocol that integrates both traditional metrics and multimodal large language model-based assessments. Extensive experiments show that IAMFlow, despite being training-free, achieves the best overall performance on NarraStream-Bench, outperforming the strongest baseline by 2.56 points, while achieving a 1.39$\times$ speedup over the most efficient baseline in the 60-second multi-prompt setting.