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NeurIPS 2026

SCDBench: A Benchmark for LLM-Based Smart Contract Decompilers

Kaihua Qin, Dawn Song, Arthur Gervais

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.

NeurIPS 2026

SC$^3$: A Multi-Solvent Solubility Challenge and Benchmark

Vansh Ramani, Tarak Karmakar, Sayan Ranu, Dhairya Kuchhal, Har Ashish Arora, Sergei V Tatarin, Lev Krasnov

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.

NeurIPS 2026

SBNO : Schrödinger Bridge Neural Operator for the Forward and Inverse Problems under Missing Information

Jaehyeon Park, Inhyeok Jeong, Noseong Park

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.

NeurIPS 2026

SAVeR$^2$: Reasoning-based Safety Alignment for Large Reasoning Models via Verifiable Rewards

Yichen Sun, LIN JIANAN, Linbo Jiang, Shiyu Wang, Zhibo Wang, Zhixuan Chu

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.

NeurIPS 2026

SAVE: Sparsity-Aware Influence Estimation for Vocabulary-Expanded LLMs

Seungyoo Lee, Giung Nam, Seanie Lee, Hyungi Lee, Juho Lee

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.

NeurIPS 2026

SAS: Simple Attention Sparsification via End-to-End Optimization of Context Ranking

Zhiwei Li, Lei Zhu, Hao Gu, Xiang Hu, Yan Wang, Haitao Mi, Sirui Han, Leo Liang, Zhijiang Guo

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.

NeurIPS 2026

SARA: Step-Adaptive Rank Adjustment for Diffusion Inference Acceleration

DongHoon Lim, Jinwoo Jung, Won-Gook Choi, Joon-Hyuk Chang

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.

NeurIPS 2026

SAMoR: Motion Modelling for Articulated Objects of Any Skeleton and Topology

Yuhao Zhang, Gerard Pons-Moll, Tolga Birdal

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.

NeurIPS 2026

SAMPPO: Structure-Aware Mirror Proximal Policy Optimization

Corinna Cortes, Mehryar Mohri, Yutao Zhong

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.

NeurIPS 2026

SAME: Stability-Aware Embedding Extraction in Mixture-of-Experts Language Models

Shufan Yang, Zifeng Cheng, Zhiwei Jiang, Hao Wang, Miao Xue, Changhui Sun, Cong Wang, Ao Zhou, Qing Gu

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.

NeurIPS 2026

SAM 3D Animal: Promptable Animal 3D Reconstruction from Images in the Wild

Xuyi Hu, Jin Lyu, Jiuming Liu, Yebin Liu, Silvia Zuffi, Liang An, Stefan Goetz

3D animal reconstruction in the wild remains challenging due to large species variation, frequent occlusions, and the prevalence of multi-animal scenes, while existing methods predominantly focus on single-animal settings. We present SAM 3D Animal, the first promptable framework for multi-animal 3D reconstruction from a single image. Built on the SMAL+ parametric animal model, our method jointly reconstructs multiple instances and supports flexible prompts in the form of keypoints and masks which enable more reliable disambiguation in crowded and occluded scenes. To train such a model, we further introduce Herd3D, a multi-animal 3D dataset containing over 5K images, designed to increase diversity in species, interactions, and occlusion patterns. Experiments on the Animal3D, APTv2, and Animal Kingdom datasets show that our framework achieves state-of-the-art results over both existing model-based and model-free methods, demonstrating a scalable and effective solution for prompt-driven animal 3D reconstruction in the wild.

NeurIPS 2026

SALART-VQA: Diagnosing Whether VLMs Understand Salient Artifacts in Generated Images

Xiaoxiao Sun, Ruotian Zhang, JUNZHE HUANG, James Burgess, Serena Yeung-Levy

Vision-language models (VLMs) are increasingly used to detect whether AI-generated images contain visible artifacts, yet their ability to analyze such artifacts remains poorly understood. A correct image-level decision can still hide important failures: a model may correctly flag an artifact while relying on the wrong visual cue, selecting the wrong region, or describing a defect that the image does not support. To evaluate these behaviors directly, we introduce SalArt-VQA, a diagnostic benchmark for fine-grained salient artifact understanding in AI-generated images. SalArt-VQA contains 950 images and 3,681 human-authored multiple-choice questions spanning artifact images, matched real reference images, and paired generated reference images. Four aligned question types evaluate presence detection, semantic localization, spatial grounding, and evidence-grounded defect identification, while the reference splits test calibration and abstention when the annotated defect is absent. Across 20 VLMs, SalArt-VQA reveals failures that image-level detection accuracy hides: the strongest model reaches 99.37% detection recall on artifact images but answers all four artifact-side questions correctly on only 53.26% of images. Comparing artifact images with defect-absent references reveals a sensitivity–calibration tradeoff: sensitive models often make unsupported artifact claims, while conservative models avoid false alarms largely by missing real artifacts. These results show that high artifact detection accuracy alone does not imply grounded artifact understanding. SalArt-VQA exposes these hidden failure modes and provides a fine-grained evaluation of whether VLM artifact claims are supported by local visual evidence.

NeurIPS 2026

SAIR: Cost-Efficient Multi-Stage ML Pipeline Autoscaling via In-Context Reinforcement Learning

Jianchang Su, Yifan Zhang, Shengkai Lin, Shizhen Zhao, Yusheng Zheng, Yiwei Yang, Wei Zhang

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.

NeurIPS 2026

SAGE: Semantically Disentangled Representation Learning through Latent Geometry Constraint and Large Language Model

Qiuyu Chen, Liang Xu, Yunnan Wang, Mingqi Yuan, Qi Wang, Jiahao Li, Zhicheng Wang, Hao ZHENG, Mulin Chen, Baao Xie, Xin Jin, Wenjun Zeng

Disentangled representation learning (DRL) aims to identify and decompose the interpretable underlying factors of observations. However, most DRL methods rely on independence-oriented regularization to pursue statistical factorization, leaving learned factors without explicit semantic grounding. Such independence-oriented regularization induces an intrinsic trade-off between disentanglement and reconstruction. To address these issues, we establish a spectral learnability threshold for independence-oriented DRL, theoretically revealing a mismatch between the factors favored by the learning objective and human-perceivable semantics. We further introduce SAGE, a semantic-guided DRL framework that leverages MLLMs to automatically extract semantic signals from unlabeled data and uses a triangular latent-geometry constraint to anchor each semantic factor to a designated latent dimension, enabling factor-wise control over human-perceivable semantic variations in generated images. Extensive experiments support our theoretical analysis and demonstrate that SAGE achieves state-of-the-art performance across multiple benchmarks. In addition, our framework exhibits high flexibility and can be integrated with most advanced generative models.

NeurIPS 2026

SAGE: Semantic-Agnostic Image Embedding for Generalized AI-Generated Image Detection

Seongho Kim, Jaehyun Choi, Dahye Kim, Sungwon Yi, Jang-Ho Choi

AI-generated image detection has become an important problem in media forensics as modern generative models produce increasingly realistic images. Recent CLIP-based detectors show strong generalization ability, but CLIP image features entangle semantic content with forensic cues. Since semantic content is not an intrinsic generation trace, relying on it can introduce semantic shortcuts and unstable performance under unseen generator shifts. In this paper, we propose SAGE, a Semantic-Agnostic Image Embedding framework for generalized AI-generated image detection. SAGE constructs paired triplets of COCO images, SDXL-reconstructed images, and COCO captions so that real and fake images share the same caption-level semantics. It then suppresses semantic information by suppressing the semantic direction provided by CLIP text embeddings from CLIP image embeddings. To enable caption-free inference, SAGE learns a semantic anchor that approximates the role of paired text embeddings at test time. In addition, SAGE adopts a SoftTriple-style sub-center classifier to model intra-class variation in real and fake images, yielding a more stable authentication geometry across diverse generators. Experiments on CommunityForensics demonstrate that SAGE achieves competitive detection performance while substantially reducing generator-wise performance variation. Further analyses show that SAGE effectively reduces semantic dependence and forms a clearer real/fake feature geometry than strong baselines.

NeurIPS 2026

SAGE: Semantic Ambiguity Guided Capacity Expansion for Retrieval-Augmented Generation

Xunlei Chen, Jinyu Guo, Yi Gong, Qirui Ye, Zhaokun Wang, Wenyi Li, Wenhong Tian

Retrieval-Augmented Generation (RAG) mitigates Large Language Model (LLM) hallucinations by grounding generation in external knowledge. Although structure-augmented RAG methods improve multi-hop reasoning, they incur high indexing costs and degrade on simple queries. We argue that this robustness gap is driven by capacity bottlenecks in fixed-dimensional inner-product scoring. As corpora grow, semantically close passages create local regions where a base scorer cannot maintain sufficient separability. Reorganizing documents into trees or graphs can expose or partially mitigate this issue, but it does not by itself provide a stronger local scoring function. We propose Semantic Ambiguity Guided Capacity Expansion (SAGE), a lightweight RAG framework that detects such regions using document-side Local Separability Deficit and expands capacity only where needed. SAGE builds a two-layer semantic index, derives atomic query views from corpus passages, and calibrates a parameter-efficient hypernetwork to generate the local Ambiguity-Conditioned Scorers for selected nodes. Extensive experiments show that SAGE improves retrieval and QA performance over dense retrievers and structure-augmented baselines, achieving a better balance between single-hop, multi-hop retrieval accuracy, and robust scalability. Anonymous Code: https://anonymous.4open.science/r/SAGE-1676.

NeurIPS 2026

SAGE: Mitigating Long-Horizon Reasoning Biases via Topological Guidance

Xinyue Zeng, Jiawei Zhang, Yujun Yan, Dawei Zhou

Long-horizon reasoning remains a central challenge for large language models (LLMs) under sparse-reward regimes. We argue that this brittleness arises from two biases induced by complex reasoning spaces: an exploration bias, where models are drawn toward locally plausible but structurally unstable branches, and a compounding bias, where small local deviations accumulate across depth and suppress rare rewards. We introduce Symbolic Closure Analysis (SCA) as a theoretical lens characterizing how branching structures and sparse rewards induce these biases in long-horizon reasoning with local admissibility, and as a design principle for structural priors in less formal reasoning tasks. Motivated by this analysis, we propose \model{} (\textbf{S}tructural \textbf{A}dmissibility-\textbf{G}uided \textbf{E}xploration), a unified framework that injects structural guidance to alleviate exploration bias and compounding bias in long-horizon reasoning. \model{} combines two complementary structural dimensions: algebraic sparsification, which projects locally admissible candidates onto operator-indexed algebraic subspaces to suppress spurious branching and mitigate exploration bias, and hyperbolic structural guidance, which embeds reasoning states into a negatively curved space to provide dense depth-wise signals and mitigate compounding bias. Across 12 benchmarks and 7 model families, \model{} outperforms competitive baselines. In particular, \model{} achieves up to an 8-fold improvement on the Andrews-Curtis problem, an open real-world long-horizon task. Code is available at: \url{https://anonymous.4open.science/r/SAGE-Long-Horizon-Reasoning-AD70}.

NeurIPS 2026

SAGAS: Semantic-Aware Graph-Assisted Stitching for Offline Temporal Logic Planning

Ruijia Liu, Ancheng Hou, Xiang Yin

Linear Temporal Logic (LTL) provides a rigorous framework for specifying long-horizon robotic tasks, yet existing approaches face a trade-off: model-based synthesis relies on accurate labeled transition systems, whereas learning-based methods often require online interaction, task-specific rewards, or specification-conditioned training. We study LTL-specified robotic planning and execution in a stricter offline, model-free setting, where the agent is given only fixed, task-agnostic trajectory fragments, with no dynamics model, task demonstrations, or online data collection. To address this setting, we propose SAGAS, a framework that combines the compositionality of symbolic synthesis with the data-driven reachability structure learned from offline trajectories. SAGAS first learns a reusable latent reachability graph and a frozen goal-conditioned executor from fragmented offline data. For each new LTL formula, it performs task-time semantic graph augmentation to ground state-defined propositions on the learned graph, and applies B\"uchi product search to synthesize a cost-aware accepting prefix--suffix waypoint plan executed by the frozen executor. By shifting formula-specific reasoning from policy learning to test-time graph augmentation and symbolic search, SAGAS enables zero-shot generalization to unseen, data-supported LTL specifications without task-specific reward design, policy retraining, or online interaction. Experiments on LTL task suites constructed from OGBench locomotion domains show that this design produces executable and cost-efficient prefix--suffix behaviors for diverse unseen LTL tasks from fragmented offline data.

NeurIPS 2026

SAFTAC: Simulation-Augmented Fine-Tuning of Open-Source LLMs for Analog Circuit Design

Junsheng Huang, Yifan Sun, Zhuoer Zhang, Ning Wei, Yening Liu, Michael Molter, Pavan Kumar Hanumolu, Elyse Rosenbaum, Bin Hu, Huan Zhang

Circuit design for analog integrated circuits (ICs) is challenging because circuit topology, device sizing, and circuit performance are tightly coupled. Existing large language model (LLM)-based methods either rely on inference-time correction around frozen models or train on limited circuit types without directly using simulation outcomes as training signals. A key bottleneck is that existing datasets do not provide sufficient task information for training LLMs on end-to-end specification-conditioned design. To fill this gap, we first construct a large-scale simulation-grounded dataset with 8,626 design tasks across 10 different types of analog circuits, where each task includes a textual description, labeled I/O ports, loading condition, target specifications, a testbench, and a reference netlist. Using this dataset, we present SAFTAC, a simulation-augmented fine-tuning framework that trains LLMs to generate and revise analog circuit design. SAFTAC combines three-stage supervised fine-tuning, which progressively builds the capabilities required for analog circuit design and feedback-guided correction, with simulation feedback-augmented reinforcement fine-tuning, which further optimizes models using simulation-grounded rewards. We evaluate SAFTAC under single-pass generation and two-pass generation with simulation feedback, showing improvements over the strongest closed-source LLM by 15.9\% and 7.8\%, respectively.

NeurIPS 2026

SAFE-Hair: Scalp-Anchored Fields for Exportable Single-View Hair Reconstruction

Yucheng Wang, Zedong Wang, Yuetong Wu, Yue Ma, Dan Xu

Single-view 3D hair reconstruction has progressed rapidly, but many methods optimize for visual plausibility rather than producing strand assets that can be directly used in grooming, editing, or simulation pipelines. We propose SAFE-Hair, a framework that reconstructs explicit strand grooms from a single portrait by decoding hair from persistent follicles in a canonical scalp-UV domain. Given a fitted template head, SAFE-Hair projects self-supervised image features onto the scalp, uses a conditional Rectified Flow model to generate latent scalp fields, and decodes occupancy, segment directions, length allocation, and total length at a fixed set of scalp follicles. This representation enforces scalp-rooted strand identities by construction and enables direct export with follicle IDs and guide-family metadata. We further introduce attachment, collision, and bending priors to improve geometric validity without per-subject test-time optimization. To evaluate both reconstruction fidelity and asset usability, we introduce HairBench-3D, a benchmark built from public 3D hair assets with standardized alignment, rendering, resampling, and metrics for geometry, direction consistency, invisible-region completion, silhouette agreement, and collision. SAFE-Hair improves Chamfer distance from 3.74 mm to 3.28 mm, F-score from 77.9 to 81.7, invisible-region F-score from 66.1 to 69.8, and penetration ratio from 4.8% to 3.9% over the strongest baseline.

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