NeurIPS 2026 Papers

Black-box watermarked LLM identification has become an important task for watermark auditing. The core challenge arises from inherent black-box constraints that deny access to logits, detector keys, model parameters, and internal watermark settings. For this reason, existing methods have not conducted in-depth exploration on this task, leaving prominent limitations: **Fixed-Reference Calibration** and **Family-Specific Tests**. First, existing works use fixed null reference to serve as a baseline for judging the presence of watermarks in LLM outputs, which might misidentify a watermarked LLM as unwatermarked because the null reference is sensitive to the prompt pair, queried model, and sampling configuration. Second, existing statistical-tests based methods adopt distinct statistical features and judgment criteria for different LLM watermark families, making them less suitable when the deployed watermark algorithm is unknown. To remedy the above two limitations, this work proposes **SCTI**, a Self-Calibrated Trident Identification framework for black-box watermarked LLM identification. Specifically, to handle the first limitation, **SCTI** constructs empirical null distributions from the queried responses to estimate the generalizable reference instead of relying on a fixed reference. Meanwhile, to address the second limitation, **SCTI** is configured with a Trident-View Consistency measuring mechanism, which enables a unified pipeline to avoid designing separate tests for individual watermark family. Experimental results verify that **SCTI** outperforms representative black-box watermark identification baselines across diverse LLMs and watermark algorithms.

Robots manipulating in cluttered spaces must often plan from partial views where foreground objects occlude the target, surrounding geometry, and usable free space. Reactive policies often miss these hidden constraints, while exhaustive search becomes computationally prohibitive in dense clutter. We present SCOUT, an occlusion-aware planning framework that bridges high-level deliberation with grounded physical rollouts. SCOUT leverages an object-centric scene representation to reconstruct hidden geometry and derive a relational planning state capturing blocker hierarchies and accessibility corridors. To navigate the vast action space in clutter, an LLM proposes structured action candidates which are then rigorously evaluated via Monte Carlo Tree Search (MCTS) using a learned action-conditioned world model. Evaluations on ‘Reveal’ and ‘Placement’ tasks show that SCOUT outperforms Vision-Language-Action (VLA) policies, embodied agents, and 3D world models, with significant gains in long-horizon tasks requiring reasoning over occluded geometry. We further validate the framework’s robustness through real-world robotic manipulation in a cluttered shelf environment.

Cross-city transfer leverages labeled data from well-instrumented cities to improve prediction in label-scarce ones, but remains challenging when cities adopt incompatible partitions with no ground-truth region correspondences. Even with expressive GNN encoders, transfer quality varies dramatically across methods sharing nearly identical backbones---indicating that alignment design, not encoder capacity, is the binding constraint. Existing paradigms exhibit complementary failure modes: heuristic anchor matching collapses to hubness under unequal partitions, while distribution-level matching over-mixes embeddings under heterogeneity. Both stem from a single missing primitive---explicit, mass-controlled soft correspondence between unequal region sets. The challenge intensifies in multi-source transfer, where independent source-to-target alignments yield conflicting gradients and source domination. We propose SCOT, which adapts entropic OT to this regime through three application-specific designs: an OT-weighted contrastive objective that resolves the geometric--semantic tension, a one-sided cycle regularizer respecting the rectangular $n_s\!\neq\!n_t$ geometry, and---as our central contribution---a shared prototype hub coordinated through balanced entropic OT under a target-induced prior, bypassing the source-selection problem in label-scarce regimes. Across real-world cities and tasks, \scot consistently improves transfer accuracy, achieving 5--50\% relative MAE/MAPE reductions over the strongest baseline, with learned couplings and hub assignments quantitatively confirming that the diagnosed failure modes are resolved.

Self-play can train language models without external supervision. However, existing methods require rule-checkable answers, leaving open-ended tasks dependent on curated prompts or frontier-model judges. We introduce SCOPE, a data-free self-play framework for open-ended tasks that co-evolves two policies: a Challenger that generates document-grounded tasks, and a Solver that answers them through multi-turn retrieval. A frozen copy of the initial model serves as the self-judge, which writes task-specific rubrics from the source document and grades Solver responses against them. Across three 7-8B instruction-tuned models (Qwen2.5, Qwen3, OLMo-3), SCOPE improves open-ended performance by up to +10.4 points on eight benchmarks and matches or exceeds GRPO_data trained on ~9K curated prompts. Although trained only on open-ended tasks, SCOPE also improves held-out short-form QA by up to +13.8 points on seven held-out benchmarks, surpassing GRPO_data on all three models. Ablations show that co-evolving the Challenger is necessary to keep tasks near the Solver's frontier, that gains arise from improvements in both retrieval and synthesis with the relative contribution varying by task, and that rubric generation quality is the bottleneck for self-judging.

Real-world scenes are compositional: bricks, blades of grass, pebbles, and tree leaves recur across human-built and natural environments. Existing neural scene representations model these elements independently. Most 3D Gaussian Splatting and follow-up abstraction and compression methods treat each element as unique, fitting millions of independent Gaussians per scene. Prior methods like Splat and Replace fit template objects, but they require mostly manual selection of repeated elements. As a result, these representations store redundant parameters and provide weak manipulation handles for downstream tasks. We introduce SCION, a hierarchical compositional scene representation that replaces independent Gaussians with a compact vocabulary of reusable primitives and lightweight world-space instances that place transformed copies throughout the scene. We fit this representation to multi-view captures via a joint optimization over discrete and continuous scene parameters, combining two-level densification over splats and instances with an adversarial loss that preserves detail across shared primitives. The recovered structure yields a compact, controllable representation while maintaining high quality even at 1.2 MB. SCION achieves rate-distortion favorable to existing Gaussian compression methods, and it enables instance-level scene editing and animation without retraining. Our results show that neural scene representations need not memorize scenes as independent primitives; they can discover reusable parts.

The exponential growth of machine learning submissions has strained the traditional peer review process, resulting in slow feedback loops for authors and an immense burden on reviewers to rigorously audit technical soundness and verify literature. To address this, we introduce ScholarPeer, a multi-agent framework designed to operationalize the rigorous auditing workflow of a senior researcher. Rather than attempting to replace human judgment, ScholarPeer serves as a co-scientist: acting as a mentor for rapid author iteration prior to submission, and as an active verification assistant that augments human reviewers. The framework structurally decouples contextualization from critique by deploying a sub-domain historian to synthesize the field's trajectory, a baseline scout to proactively hunt for omitted state-of-the-art comparisons, and a multi-aspect Q&A engine that deeply audits technical soundness—scrutinizing internal logical consistency, experimental validity, and mathematical rigor—while cross-referencing claims against top-tier academic venues. We comprehensively evaluate ScholarPeer on $\sim$1,800 ICLR submissions spanning 2020 through 2025. Our results show that ScholarPeer achieves significant win-rates against state-of-the-art fine-tuned models and search-augmented agentic baselines.

Video Emotion Recognition (VER) aims to identify and understand the emotional states of characters by analyzing visual and auditory information in videos. However, conventional rigid frame sampling strategies for long videos tend to fragment continuous emotional expressions. Moreover, existing fusion methods often fail to properly model temporal dependencies and may suffer from future information leakage. To address these limitations, we propose SCG-HF, a novel framework that preserves semantic consistency through adaptive grouping and hierarchical fusion. Specifically, we introduce Semantic Consistency Grouping (SCG), which dynamically segments videos into semantically coherent units based on feature similarity, thereby maintaining the integrity of emotional patterns. Furthermore, we design a three-level Hierarchical Fusion (HF) architecture to capture emotional dynamics at different temporal granularities: (i) frame-level refinement enhances subtle local emotional cues; (ii) segment-level history-guided fusion models temporal evolution across segments while strictly preventing future information leakage via attention; and (iii) sample-level contrastive alignment synchronizes audio and visual representations in a shared latent space. Extensive experiments on the VideoEmotion-8 and Ekman-6 benchmarks demonstrate that SCG-HF achieves state-of-the-art performance.

Causal discovery from nonlinear multivariate time series is challenging in high-dimensional systems, where the number of candidate directed relations grows quadratically with the number of variables. Existing methods are often limited by target-wise model fitting, repeated conditional testing, or expensive graph-search procedures. We propose SCDM, a shared-parameter meta-learning framework for scalable temporal causal discovery. SCDM treats each target variable as a task while learning a shared temporal predictor and a shared causal-strength matrix across tasks. Each meta-episode updates only a subset of target-variable tasks, allowing the model to accumulate structural evidence across the full system without exhaustive target-wise optimization. After training, a post-training graph readout converts the learned predictor into continuous directed causal scores for threshold-independent evaluation. We also provide a finite-error recovery principle showing that, under fully observed delayed-system assumptions and a positive risk-gap condition, thresholding SCDM scores recovers the graph when statistical, meta-optimization, and readout errors are below the population margin. Experiments on controlled synthetic, neuroimaging-inspired, high-dimensional, and real-world temporal benchmarks demonstrate competitive causal recovery and improved scalability in large temporal systems.

Smart contract decompilation aims to recover high-level source code from bytecode, but evaluating decompilers remains difficult because existing studies use narrow datasets, inconsistent metrics, and limited semantic consistency checks. This gap is increasingly important as large language models (LLMs) begin to generate source-like Solidity that may compile and appear plausible, even when its semantics diverge from the original contract. We introduce SCDBench, a dataset and benchmark methodology for LLM-based smart contract decompilation. The dataset contains $600$ real-world Solidity contracts with paired bytecode inputs, ground-truth source code, and replayable semantic checkpoints. SCDBench evaluates decompiler outputs through four cumulative stages: format completeness, compilability, Application Binary Interface (ABI) recovery, and semantic consistency via differential replay. We evaluate Claude Opus 4.7, GPT-5.3-Codex, and GLM-5 in a zero-shot decompilation setting, including GLM-5 variants with and without extended reasoning and a zero-shot compilation-repair setting. The results show that frontier LLMs can often produce structured and compilable Solidity, but achieving semantic consistency remains far from solved: the best-performing frontier model perfectly decompiles only $42/600$ contracts. We further show that introducing same-model compilation repair substantially improves performance at modest additional cost. SCDBench establishes a common ground for rigorous, reproducible evaluation and aims to accelerate the development of reliable smart contract decompilers for blockchain security and transparency.

Solubility prediction is a standard benchmark in computational chemistry, yet multi-solvent models which reportedly approach the experimental-noise ceiling (i.e. the aleatoric limit) are not yet reliable enough to be deployed. We argue that this gap is partly artefactual: published benchmarks differ in curation policies, evaluate on count-weighted RMSE that hides failure on tail-heavy solvent distributions, and treat the widely cited 0.6–0.8 log S inter-laboratory figure as the aleatoric ceiling even though it reflects worst-case, not expected, disagreement. We introduce SC3, a multi-solvent solubility benchmark built on BIGSOLDB v2.1 with three contributions: (i) a reproducible curation pipeline yielding 101,535 measurements over 1,327 solutes and 206 solvents, with a recalibrated aleatoric floor of 0.106 log S—roughly 6× tighter than the conventional figure; (ii) nested Gold/Silver/Bronze consensus tiers with per-point σ\sigma σ, three leakage-checked splits, and a multi-solvent metric suite (PS-RMSE, Z-RMSE); and (iii) a 31-model benchmark across six families, whose best Bronze PS-RMSE sits at ≈5×εaleatoric\approx 5 \times \varepsilon_{\text{aleatoric}} ≈5×εaleatoric, and we observe this is a gap unclosed by any deep alternative tested. We perform three follow-on analyses: data scaling, transfer from quantum-chemistry solvation energies, and feature-level attribution, which demonstrates that calibrated per-point uncertainty is a reusable infrastructure for diagnosis beyond point prediction.

Partial differential equation (PDE) surrogate modeling often relies on either dense observations or access to governing equations/physics constraints. In practice, observations can be sparse and partial, and the underlying physics may be partially specified or unavailable. We introduce SBNO, an equation-free paired‑data diffusion bridge matching framework that learns conditional operators for both forward and inverse PDE problems under partial observations. SBNO learns a pair of time-dependent forward/backward drifts via diffusion Schrödinger bridge matching, using a unified endpoint coupling that preserves empirical input-solution pairing, enabling both directions to be handled within a single framework. At inference time, SBNO performs deterministic reconstruction by integrating the probability-flow ODE of the learned bridge, eliminating the need for inference-time hyperparameter tuning. On five PDE benchmarks with 5\% observations, SBNO achieves superior or comparable accuracy to baselines while demonstrating lower test-instance error dispersion. Furthermore, SBNO is at least $9.7\times$ faster and reduces GPU memory by at least $4.3\times$ compared to diffusion-based baselines.

Large Reasoning Models (LRMs) have achieved impressive performance via explicit Chain-of-Thought (CoT) reasoning, yet this process introduces critical safety risks. We formalize LRM safety objectives as requiring non-malicious reasoning and answers, safety consistency, and explicit intent identification. We argue that existing alignment methods suffer from poor adversarial generalization, a significant ``safety tax'' on reasoning, and unfaithful coarse-grained rewards, primarily due to their neglect of the LRM's intrinsic reasoning capabilities for safety. To address these, we propose \textbf{SAVeR$^2$}, a novel \textbf{S}afety \textbf{A}lignment method via \textbf{R}ewards with \textbf{R}easoning capability. SAVeR$^2$ decomposes the safety objective into four fine-grained verifiable reward signals and utilizes a difficulty-aware data selection strategy to stabilize training. Crucially, we introduce a reasoning-preserving gradient projection mechanism that analytically resolves conflicts between safety and reasoning gradients, effectively eliminating the safety tax. Extensive experiments on models ranging from 1.5B to 671B demonstrate that SAVeR$^2$ achieves superior safety performance and significantly lower over-refusal rates across multiple benchmarks without compromising general reasoning capabilities.

Large language models adapted to specialized targets such as Lean theorem proving or tool calling often benefit from extending the tokenizer with target-specific tokens. However, target-only vocabulary-expanded fine-tuning can leave the newly added embeddings transfer-incomplete, since the new rows receive gradients only from examples containing the corresponding tokens, so target-local gains do not necessarily imply that the embeddings are integrated with related capabilities. We propose SAVE, a sparsity-aware influence estimator for selecting auxiliary healing data from generic corpora. SAVE computes token-conditional curvature over the effective update set of each new vocabulary row and combines this vocabulary-aware signal with dense LoRA-level influence. Across Lean autoformalization and tool calling, SAVE improves target performance and related transfer while remaining competitive on broad retention benchmarks.

Post-training attention sparsification reduces the quadratic cumulative attention cost of pretrained Transformers by selecting a small set of context units (tokens or blocks) for each query. Existing trainable methods usually rely on a lightweight selector to score context units followed by a hard Top-$K$ selection, which blocks gradients from the language modeling loss. As a result, these methods commonly resort to distilling layer-wise dense attention distributions. While this approach encourages the selector to rank context units according to dense attention weights in the original model, such a ranking is not directly aligned with their impact on the model's final predictions under a fixed attention budget (ie, the number of attended context units per query), which can waste the limited attention budget on less useful units. To address this ranking misalignment, we propose **S**imple **A**ttention **S**parsification (SAS), a gated sparse attention mechanism that optimizes context ranking end-to-end with the language modeling loss. The key idea is to inject the selector's continuous scores into the attention logits during training, allowing the language modeling loss to update the selector through standard backpropagation. We identify several choices that are crucial to make this simple design work well in practice: placing the gate inside the attention $\operatorname{softmax}$ in log form, keeping all context units active during training so each of them can receive gradients, and preserving continuous selector scores so the model learns relative priorities rather than only hard selections. Since a naive implementation that explicitly materializes the full attention matrix would cause prohibitive memory overhead for long sequences, we implement a memory-efficient Triton kernel that integrates the design into a FlashAttention-style computation. SAS consistently outperforms trainable sparse-attention across attention budgets, with especially large accuracy gains under tight budgets, demonstrating more effective context ranking for downstream tasks.

Diffusion models achieve strong performance across image and audio synthesis, but their inference remains computationally expensive, requiring tens to hundreds of sequential evaluations of a large denoising network. We observe that the effective rank of the denoiser output varies sharply across timesteps and architectures, motivating step- and block-wise adaptation of computation. We propose Step-Adaptive Rank Adjustment (SARA), a training-free framework that treats the SVD rank of pretrained linear layers as an inference-time control variable, adapted per step and block by SARA-DP and per step by SARA-Online. SARA introduces the Low-Rank Approximation Error (LRAE) — the discrepancy a rank-r truncation induces against the full-rank network — and instantiates it in two algorithms: SARA-DP measures LRAE on a few warmup prompts and solves the globally optimal rank assignment under a MACs budget by dynamic programming; SARA-Online computes a closed-form, spectrum-based LRAE from the model output at inference time, requiring no calibration and no trajectory lookahead. SARA applies uniformly to UNet (SDXL), MMDiT (SD3), DiT (Stable Audio Open), and hybrid MMDiT/DiT (TangoFlux) backbones, spanning ϵ, v, and rectified-flow parameterizations. SARA combines with existing solver- and cache-axis acceleration to yield compounded wall-clock speedups in our experiments. Together, these results introduce rank as a new axis of diffusion inference acceleration.

Modeling motion for articulated objects of arbitrary skeleton topology remains difficult: existing motion generators target a fixed human skeleton, and prior adaptations either fail to share a vocabulary across rigs or discard motion detail through global pooling. Our key observation is that while joint-level motion does not correspond cleanly across species, motion of functional joint groups does---a human arm, a wolf foreleg, and a bird wing share semantic motion structure despite differences in joint count and connectivity, a correspondence that joint names (e.g., ``forearm'', ``wing\_L1'') partially expose even when topology does not. We introduce SAMoR (Skeleton-Aware Motion Representation for Articulated Objects), a cross-topology motion representation that encodes each motion segment as a small fixed number ($K{=}8$) of part tokens shared across arbitrary skeletons. SAMoR takes three input signals---per-joint motion features, kinematic graph structure, and joint-name embeddings---and processes them with a graph-transformer encoder, then compresses the resulting heterogeneous per-joint features into part-level tokens via cross-attention pooling and residual vector quantization, yielding a discrete motion codebook shared across rigs. To prevent the part queries from collapsing into redundant global representations, we introduce a topology-agnostic attention supervision loss, combined with random joint-name dropout to prevent over-reliance on text labels; together these encourage the part tokens to cluster joints into functional groups from names, structure, and motion jointly. We curate a unified heterogeneous motion corpus from HumanML3D, Truebones Zoo, and animated Objaverse-XL assets, and evaluate SAMoR on held-out characters with unseen skeletons. The resulting representation supports accurate reconstruction and cross-topology motion transfer, and further enables text-conditioned generation and localized part-wise editing through a MaskGIT token generator. SAMoR reaches $2.75\!\times\!10^{-2}$ normalized MPJPE on cross-topology reconstruction---$5.8\!\times$ below the strongest adapted variable-$J$ tokenizer baseline---and remains competitive with fixed-skeleton specialists on HumanML3D for both VQ-VAE reconstruction and text-to-motion generation.

The alignment of Large Language Models (LLMs) via reinforcement learning from human feedback (RLHF) typically relies on Proximal Policy Optimization (PPO), which uses a uniform clipping hyperparameter $\epsilon$ to maintain a stable trust region. However, we demonstrate that this uniform constraint is ill-suited for the non-uniform semantic topology of natural language. In ``semantically dense'' regions, where candidate tokens act as semantic synonyms within the current context, noisy advantage estimates can destabilize the fixed trust region, leading to *policy collapse*, a severe loss of generative diversity and policy entropy. To resolve this, we propose *Structure-Aware Mirror Proximal Policy Optimization (SAMPPO)*, which introduces an adaptive trust region governed by local semantic density. SAMPPO generalizes the trust-region framework to non-uniform semantic spaces by adapting the mirror-descent geometry to the local action topology. SAMPPO uses a per-timestep *Semantic Isolation Score* to dynamically tighten the clipping range in dense regions and relax it in sparse ones. We provide theoretical foundations for this approach, deriving a *Structure-Aware Monotonic Improvement* bound and establishing a formal link to structure-aware margin-shifted $H$-consistency theory. Empirically, SAMPPO prevents the entropy collapse observed in standard PPO baselines and significantly improves alignment. Evaluated on the Anthropic HH-RLHF and NVIDIA HelpSteer benchmarks across multiple model architectures (Llama-3-8B and Gemma-2-2B), SAMPPO achieves robust, zero-shot generalizable alignment gains. Crucially, it strictly outperforms standard PPO and recent empirical adaptive heuristics (BAPO), demonstrating competitive performance against direct preference baselines including DPO and SimPO on the alignment-diversity Pareto frontier.

Extracting sentence embeddings from MoE-based large language models is a promising direction, as they provide greater model capacity than dense models at comparable computational cost. Existing works perform the standard forward propagation process to extract embeddings, overlooking a key stability requirement in the MoE encoding process: semantically similar texts should be encoded into similar representations. In this work, we identify two key stability-related phenomena in MoE models: (1) routing stability varies across layers, and (2) shared and routed experts exhibit different levels of stability. To address these issues, we propose SAME, a Stability-Aware MoE Embedding extraction framework that dynamically allocates activated experts across layers and adjusts expert outputs to mitigate component instability and improve embedding quality. Specifically, SAME assigns fewer activated experts to layers with less stable routing, where layer-wise routing stability is measured by the average overlap between the experts activated before and after injecting Gaussian noise into each layer’s router inputs. Additionally, SAME reduces the contributions of routed experts to alleviate the impact of their instability. Notably, our method is training-free, seamlessly integrates with existing approaches, and incurs no additional inference overhead. Experiments on semantic textual similarity benchmarks demonstrate that SAME consistently improves the quality of extracted embeddings across multiple MoE backbones.

Multi-stage ML inference pipelines are difficult to autoscale because of heterogeneous resources, cross-stage coupling, and dynamic bottleneck migration. Production controllers ignore cross-stage effects, and learning-based autoscalers require offline training data that is unavailable for new pipelines. We present SAIR, an autoscaler for the zero-shot deployment regime that uses an LLM as an in-context reinforcement learning controller, improving its policy online from reward-labeled experience tuples in the LLM's context with no gradient updates. SAIR combines Pareto-dominance reward shaping with a provable separation margin between dominated and non-dominated actions, Bayesian-surprise experience retrieval, and a user-space CUDA interception layer that delivers continuous GPU rate control with sub-millisecond actuation overhead. We prove a regret bound that decomposes into retrieval coverage and LLM selection components, each mapping to a concrete SAIR mechanism. On four ML serving pipelines under three workload patterns, SAIR achieves the best or tied-best P99 latency in every configuration (up to 50\% reduction over the next baseline); billable cost is reduced through replica consolidation, and effective cost (which captures GPU rate-control savings under co-location or MIG) by up to 97\%. SAIR reaches 86\% bottleneck detection accuracy without any offline training.