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版本更新 · v3arXiv:2601.21623

LAMP: Look-Ahead Mixed-Precision Inference of Large Language Models

Stanislav Budzinskiy, Marian Gloser, Tolunay Yilmaz, Ying Hong Tham, Yuanyi Lin, Wenyi Fang, Fan Wu, Philipp Petersen

原文摘要

Mixed-precision computations are a hallmark of the current stage of AI, driving the progress in large language models towards efficient, locally deployable solutions. This article addresses the floating-point computation of compositionally-rich functions, concentrating on transformer inference. Based on the rounding error analysis of a composition $f(g(\mathrm{x}))$, we provide an adaptive strategy that selects a small subset of components of $g(\mathrm{x})$ to be computed more accurately while all other computations can be carried out with lower accuracy. We then explain how this strategy can be applied to different compositions within a transformer and illustrate its overall effect on transformer inference. We study the effectiveness of this algorithm numerically on GPT-2 models and demonstrate that already very low recomputation rates allow for improvements of up to two orders of magnitude in accuracy.

cs.LG · math.NA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07159

Beyond Proof of Possession: A Relational Theory of Authority

Nicola Gallo

原文摘要

Proof-of-Possession (PoP) establishes that an executor controls an artifact (key, token, credential, or capability), but such control alone determines neither the executor's relational profile nor the causal history of an execution occurrence. We separate PoP from Proof of Relationship (PoR), the semantic property that one execution occurrence validly continues another, and from authorization, which is occurrence-sensitive. The foundational change is in the semantic domain of authorization: from holders and authority-bearing artifacts to execution occurrences situated in causal histories. We give authorization models an observation-based occurrence verdict and a separate transition-admissibility relation; kernel inclusion yields a partial order on observation-kernel classes. Building on Provenance Identity Continuity (PIC) and Proof-of-Continuity (PoC), we classify executors by authorization-relevant equivalence relations whose quotients form relational profile spaces, with positive predicates over profile transitions forming a distributive lattice. This adds relational attenuation to authority attenuation: along a valid causal history, $C_{i+1}\subseteq C_i$ and $ρ_{i+1}\Rightarrowρ_i$. We prove dual monotonicity, combined closure, and origin relational closure. Two occurrences of the same authenticated executor, with the same profile and possession evidence, need not be authorization-equivalent, and within a valid PIC lineage a deputy's independent authority cannot be imported outside the origin context. Finally, we prove that the executor-dependent decision factors through the relational profile and is invariant under sound mechanisms establishing it: successor-side PoP is a possible evidence mechanism, not a necessary premise of authorization. Under explicit Witness Assumptions, a boundary-witnessed checkpoint construction realizes the premises without successor-side PoP.

cs.CR · cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07156

Lossless Source Coding with Marginal Distribution Constraint

Cheuk Ting Li, Jun Chen, Shahab Asoodeh

原文摘要

We consider lossless source coding with a marginal distribution constraint on the encoding symbols (SC-MDC). Unlike distribution matching, signal shaping and homophonic coding which require the symbols to be (approximately) i.i.d. following a target distribution, SC-MDC requires each symbol to have a marginal distribution that exactly matches the target distribution, which can be regarded as a perfect perceptual quality constraint on the symbols, and may have applications to wear leveling, source coding with cost and steganography. We give nonasymptotic lower and upper bounds on the optimal encoding length, and exactly characterize the coding rate (or the first-order term of the length) when the source distribution or the target distribution is uniform, or when the source is an i.i.d. sequence.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07155

Depth or Description? When a One-Step Latent Score Replaces Lookahead in Proactive Information Acquisition

Biswa Sengupta

原文摘要

A proactive agent must decide what to ask before it acts. Exact lookahead values combinations of questions but is expensive and leans on a fitted model; a one-step score is cheap but can miss them. Our main finding: the need for depth often lies in how the hidden cause is described. A one-step score on the label can be blind to an interaction among answers. A score on a suitable latent cause is not, and where that latent encodes the decision it replaces most of depth at a fraction of the search. Described differently, the same problem can defeat the score while exact values do not move, so keep the latent small and check it against exact values. Probabilities learned from one language model's answers do not carry to another unless planning allows for both; those read from tokens are worth only a few labels.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2401.16754

Human Responses to Algorithmic Correction: Evidence from Centre Court

David Almog, Romain Gauriot, Lionel Page, Daniel Martin

原文摘要

We study how algorithmic review changes error composition using Hawk-Eye records from professional tennis. Within 20 mm of the line, mistaken "in" call rates increase by 8.6 percentage points, while mistaken "out" call rates decrease by 4.1 points. Within matches, calls shift toward avoiding previously overturned errors. We find no evidence that initial accuracy deteriorates within 100 mm of the line, despite concerns that workers may rely on correction as a safety net. We derive an investment framework incorporating human responses alongside correction benefits and remedy costs, showing that holding human decisions fixed can misstate the organizational returns to review.

cs.LG · cs.CY · econ.GN

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07149

FastAFD: When Does Attention-FFN Disaggregation Pay on Rack-Scale NVLink?

Yichao Fu, Yuxuan Zhang, Minshen Zhang, Ruitian Wang, Junda Chen, Hao Zhang

原文摘要

Attention-FFN disaggregation (AFD) separates attention from the experts of a mixture-of-experts (MoE) model and has been reported to raise decode throughput over colocated serving. Whether this advantage holds on rack-scale NVLink systems such as GB200 NVL72 remains unclear: prior results were obtained on other hardware, against narrower colocated baselines, or with analytical models, whereas production engines serve large MoE models on these systems with wide expert parallelism (wide EP). We build and tune FastAFD, an AFD engine for GB200 NVL72 that overlaps the MoE of one microbatch with the attention of the other, and compare it against the best vLLM wide EP setting. Experiments show that AFD has a throughput advantage of up to 1.17x over wide EP on Qwen3 and 1.16x on Qwen3.5, and matches it on MiniMax-M2.5. We derive an analytical model that predicts AFD throughput within 3% using only profiles of colocated vLLM, telling whether switching to AFD would pay off before deploying it. The model yields a concise insight: AFD's speedup factors into a batch gain (larger batches, since attention GPUs hold no expert weights), an overlap gain (shorter steps, since the MoE runs on the expert GPUs behind attention), and a disaggregation cost (the GPUs given to experts). AFD has the advantage when the two gains outweigh the cost. This view explains which models benefit from AFD, and shows that speculative decoding helps light-expert models most, where the enlarged overlap gain outweighs the larger disaggregation cost and adds a further 8% to AFD's advantage over wide EP. Looking ahead, serving the experts on accelerators that keep their weights in on-chip SRAM, such as the Groq LPX accelerators paired with Vera Rubin NVL72, would remove most of the disaggregation cost, raising the ideal speedup to 2.48x with speculative decoding. FastAFD is open source at https://github.com/hao-ai-lab/FastAFD.

cs.DC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.37314

FLASH: A "Generate Once, Synthesize Many" Framework for Synthetic Anomaly Generation in Industrial Anomaly Detection

Abhay Kumar Das, Rajesh Gangireddy, Ashwin Vaidya, Samet Akcay

原文摘要

Synthetic anomaly generation helps expand industrial anomaly datasets when real defects are scarce or unavailable. Existing approaches lie at two extremes: procedural approaches are fast but struggle to represent complex anomalies, while generative approaches produce diverse defects but require costly per-sample generation. We present FLASH, a framework that decouples defect generation from anomaly synthesis under a ``generate once, synthesize many'' paradigm. Given only normal images, FLASH uses Vision-Language Model (VLM) guidance and an image-generation model to produce a small set of defect images, from which it extracts, validates, and banks reusable defect patches. For synthesis of anomalous images, Object Boundary Suppression (OBS) first identifies the probable foreground object-aware region of the host image, while Multi-Resolution Spectral Pyramid (MRSP) noise generates diverse, size-controllable masks that determine the defect location and spatial extent. It then composes a large and diverse synthetic anomalous image set by localizing the defect region, sampling size-controllable placement masks and seamlessly blending retrieved defects onto new defect-free images without further need for image generation. Experiments on the MVTec AD 2 dataset show that FLASH-generated anomalies nearly close the calibration gap on real defects, reaching 78.1% image-level F1 against an 83.6% real-anomaly upper bound and providing the most consistent calibration transfer across detectors among procedural and generative alternatives. Moreover, FLASH synthesizes anomalies more than 11.95x faster than per-sample generative approaches.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2509.25549

Hybrid Approach for Enhancing Lesion Segmentation in Fundus Images

Mohammadmahdi Eshragh, Emad A. Mohammed, Behrouz Far, Ezekiel Weis, Carol L Shields, Sandor R Ferenczy, Trafford Crump

原文摘要

Choroidal nevi (CN) are common benign fundus lesions that require longitudinal monitoring because a small fraction transform into melanoma. Accurate delineation at native resolution is challenging: CN typically occupy <2% of the image, boundaries are indistinct, and expert annotations are limited. We propose a hybrid pipeline in which an unmodified UNet trained on 128 * 128 images provides a coarse location prior, while a training-free superpixel method (SLIC) recovers the boundary at the full input resolution. The prior further drives automatic per-image selection of the superpixel count via an image-to-lesion ratio, eliminating manual tuning. On 25 held-out 1024 * 1024 images, the proposed method achieved a mean Dice of 90.9% (IoU 87.9%), outperforming nnU-Net (83.2%), Swin UNet (73.0%), and Attention UNet (60.6%) trained at the same resolution, with all paired differences significant after Holm correction. On 90 external images from a second centre, the advantage persisted (76.8% vs. 69.3% for nnU-Net). Ablation studies identify the automated superpixel count as the key component. Inference needs no GPU, requiring 1.6 s per 1024 * 1024 image and 15 s at 3900 * 3900 on a laptop.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07148

Perceptual Distortion for Overfitted Video Coding: Sliced Matching in Space, Error Alignment in Time

Tianyi Yang, Ge Gao, Fan Zhang, David Bull

原文摘要

Recent works in neural video coding have increasingly been trading off perceptual quality against reconstruction fidelity at a fixed bitrate. Wasserstein Distortion (WD) offers a principled approach to balancing these objectives by relaxing spatial correspondences in feature matching, enabling lightweight overfitted image codecs to compete with computationally intensive generative decoders. Extending vanilla WD to video, however, presents two challenges: its independent Gaussian approximation limits the expressiveness of feature distribution matching, while unconstrained texture resampling across frames introduces temporal inconsistencies. We address these challenges by distinguishing between the correspondences that can be relaxed and those that must be preserved. Spatially, we introduce a local sliced Wasserstein distortion (SWD) that matches empirical feature distributions through one-dimensional optimal transport, capturing cross-channel dependencies without enforcing exact spatial alignment. Temporally, we employ a novel flow residual that constrains coding errors along motion trajectories, preserving temporal consistency without imposing additional reconstruction fidelity constraints. Evaluated on UVG and HEVC Class B with the overfitted codec COOL-CHIC, our framework achieves BD-rate reductions of 9.2% in DISTS and 3.0% in LPIPS relative to frame-wise WD, averaged over test sequences, while lowering tOF by 10.2% at matched rate.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.20457

Oracle-Robust Online Alignment for Large Language Models

Zimeng Li, Mudit Gaur, Vaneet Aggarwal

原文摘要

We study online alignment of large language models under misspecified preference feedback, where the observed preference oracle deviates from an ideal but unknown ground-truth oracle. The online LLM alignment problem is a bi-level reinforcement problem due to the coupling between data collection and policy updates. Recently, the problem has been reduced to a tractable single-level objective in the SAIL (Self-Improving Efficient Online Alignment) framework. In this paper, we introduce a pointwise oracle uncertainty set in this problem and formulate an oracle-robust online alignment objective as a worst-case optimization problem. For log-linear policies, we show that this robust objective admits an exact closed-form decomposition into the original loss function plus an explicit sensitivity penalty. In the finite-response log-linear setting, we develop projected stochastic composite updates for the resulting weakly convex objective and prove $\widetilde{O}(\varepsilon^{-2})$ oracle complexity for reaching approximate stationarity.

cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03625

FADE: Factored Ability--Difficulty Filtering for Online RLVR Scheduling

Tomas Duncun, Jonah Ellison, Mateo Ruiz, Hanae Fujita, Ingrid Solberg

原文摘要

Group-based reinforcement learning with verifiable rewards (RLVR) learns only from prompts whose sampled completions disagree: all-pass and all-fail groups produce zero relative advantage and burn generation budget without moving the policy. Adaptive schedulers try to steer rollouts toward informative prompts, yet they rely on per-prompt pass-rate trackers that entangle two quantities evolving at very different speeds---the policy's shifting competence across reasoning skills and each prompt's intrinsic difficulty. When a single exponential average or Beta counter tracks both at once, short memory scrambles the relative ordering of prompts whereas long memory reports stale pass rates. We introduce FADE (Factored Ability--Difficulty Estimation), a bilinear state-space filter that separates the two timescales. FADE represents each prompt's success logit as the inner product of a whitened $K$-dimensional skill loading with a fast-drifting policy ability vector, minus a slow-shrinking scalar item intercept. Using Pólya--Gamma augmentation, each minibatch of binary rollout outcomes updates the $K$-dimensional ability posterior in closed form via a rank-$K$ Woodbury step, instantly transferring competence shifts to all unobserved prompts that share those skills while leaving item intercepts stable. From the resulting posterior mean and variance we derive a closed-form utility for the probability of drawing a non-degenerate rollout group. Across five reasoning benchmarks, two base models, and four scheduling paradigms under a matched rollout budget, FADE consistently improves downstream accuracy and calibration while adding less than $0.6$\,ms per step.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.00749

PeptSol: A Translational Benchmark for Protected-Peptide Solubility in Drug Manufacturing

Alexander Arjun Lobo, Marc Saouda, Urvi Awasthi, Leonid Zhukov

原文摘要

Liquid-phase peptide synthesis (LPPS) offers a scalable route to peptide manufacturing, but route development still depends on empirical solvent screens because each protected intermediate must be soluble for reaction and recoverable for purification. The protecting groups, terminal modifications, and noncanonical residues that govern this behaviour are poorly represented in existing peptide property benchmarks, limiting the ability to develop and rigorously evaluate molecular representation models for LPPS-relevant prediction tasks. We introduce PeptSol, an open benchmark of 1388 hand-curated protected-peptide solubility measurements with chemistry-aware splits. The measurements span only 84 parent sequences and 101 protected structures, and 92.5% come from a single historical programme. Across seven frozen molecular encoders, the best macro-F1 is 0.843 on binary solubility for the historical subset under a peptide-held-out split; the harder three-class ordinal task reaches 0.614 in that cell. No foundation model consistently outperforms a baseline combining chemical fingerprints with explicit molecular descriptors, and model rankings vary across prediction tasks and data splits. The dataset, evaluation protocol, and baselines establish a standard for measuring progress on molecular representations for synthesis-aware peptide developability.

cs.LG · physics.chem-ph · q-bio.BM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07140

More Agents, More Reliable? System-Level Reliability of Multi-Agent LLMs under Operational-Profile Uncertainty

Burkay Tunçtürk, Koorosh Aslansefat, Yiannis Papadopoulos, Bao Nguyen

原文摘要

Teams increasingly build software from cooperating large language model (LLM) agents, often assuming more agents mean more reliability. Yet the usual evidence, average benchmark accuracy, ignores the input mix a deployed system meets and the rule merging the agents' answers. Building on HIP-LLM, a hierarchical imprecise-probability assessment of single LLMs, we propose a framework combining it with a Bayesian network of the system's failure logic for multi-agent systems. Agent failure-probability intervals propagate through this network, where the combining rule can itself fail, into a reliability envelope showing which evidence limits it. In a pipeline extracting regulated chemical-hazard statements, majority voting over three models protected against the weakest but did not clearly exceed the best, and a voter failing on 5\% of demands would largely erase its advantage over the middle model. More agents yield a more defensible reliability claim, not automatically a more reliable system.

cs.MA · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.33258

Two Uncertainties Are Not One: Disagreement versus Decision-Risk in Cheap Structured Content Moderation

Karan Yadav

原文摘要

Content moderation compresses a graded, contested human judgment into a binary act (leave up / take down) over billions of items, at a scale where running a frontier LLM on every post is uneconomical. A new class of frozen "System One" models returns typed, calibrated decisions instead of text at ~USD 0.000025 per call and ~100 ms. We run the first independent audit of one such model on 142,658 human-labeled comments, and benchmark it against 14 frontier reasoning models (the GPT-5.x, Claude, Gemini 3, and Grok families) on a shared probe set. Two findings stand out. First, the cheap model is accuracy-competitive: its zero-shot classification AUROC (0.83) is not statistically separable (paired bootstrap, n=56; a failure to reject parity, not proof of equivalence) from 13 of the 14 frontier systems while costing 1-3 orders of magnitude less, and it is no worse calibrated than several of them. Second, "confidence" conflates two uncertainties that come apart: human disagreement (where annotators split on satire and coded language) and decision risk (where the automatic action is costly-wrong). Under an asymmetric moderation loss these call for opposite escalation signals, so no single confidence number serves both. Because the model prices extra questions at nearly zero, decomposing the judgment into parallel sub-questions and reading their spread recovers a human-disagreement signal that the model's native confidence misses -- but this remedy is specific: it helps the weakest, disagreement-blind models and only when the decomposition aligns with the sources of disagreement; a specialist toxicity model and the strongest LLMs do not need it, and the effect is distribution-sensitive. We will release the instrument, the cached runs, and the analysis.

cs.CL · cs.CE · cs.LO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.01483

Murmur: Efficient Long-Context ASR Inference with Hierarchical Chunking

Wei-Tzu Lee, Keisuke Kamahori, Baris Kasikci

原文摘要

Long-form automatic speech recognition (ASR) requires both high accuracy and low inference latency, but existing systems fall short of handling the trade-off between the two. Chunk-based approaches process audio in parallel windows for low latency, but lose cross-chunk context and need brittle heuristics to align speakers and timestamps at boundaries. Long-context ASR models transcribe everything in a single pass for better accuracy, but run several times slower, and a single repetition loop can corrupt the entire output. In this paper, we first analyze how context length affects the accuracy of long-context ASR models and find that an intermediate chunk size of about five minutes matches single-pass accuracy while avoiding repetition loops. Building on this, we propose Murmur, an inference system for long-form ASR based on hierarchical chunking. The long-context model decodes intermediate target chunks in parallel, and a fast short-window ASR on 30 s draft windows accelerates each chunk with lossless, structure-aware speculative decoding. The short-window ASR drafts the transcript, simple format rules draft the timestamps and speaker tags, and the long-context model verifies every token. On two long-context ASR models and four meeting datasets in English and Mandarin, Murmur cuts end-to-end latency per recording by 2.5x on average (1.9-2.9x) relative to single-pass decoding, including diarization, while lowering tcpWER by 1.9 points on average. Our code is available at https://github.com/uw-syfi/Murmur.

cs.LG · cs.AI · eess.AS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.13033

DenseReward: Dense Reward Learning via Failure Synthesis for Robotic Manipulation

Yu Fang, Wanxi Dong, Jiaqi Liu, Yue Yang, Mingxiao Huo, Yao Mu, Huaxiu Yao, Li Erran Li 等 10 位作者

原文摘要

Reinforcement learning holds great promise for improving robot policies beyond the limits of imitation learning. However, its practical adoption remains bottlenecked by the lack of reliable vision-language reward models that provide dense and informative feedback. Two key challenges remain: acquiring diverse failure data at scale and obtaining fine-grained reward signals beyond sparse trajectory-level success labels. Existing approaches reuse successful trajectories to construct negative or suboptimal examples through trajectory truncation, instruction relabeling, or trajectory comparisons, without generating new trajectories where physical interaction failures such as collisions, missed grasps, object drops, and recovery actually occur. Meanwhile, many existing reward models predict sparse binary or trajectory-level rewards, which provide limited guidance for efficient policy optimization. We introduce DenseReward, a dense robotic reward model that addresses both challenges. To train DenseReward, we develop an automated failure data generation pipeline that synthesizes physically realistic failure trajectories in simulation without manual annotation, covering diverse failure modes such as collisions, missed grasps, object drops, and recovery behaviors. DenseReward predicts dense frame-level reward scores from visual observations and language instructions, enabling fine-grained estimation of task progress throughout an episode. Experiments show that DenseReward outperforms general-purpose VLMs and existing robotic reward models in dense reward prediction across both simulated and real-world manipulation. We further demonstrate that DenseReward provides effective reward guidance for downstream model predictive control and reinforcement learning.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07134

Matriarchy Classics: A Computational Approach to Swap Only Gendered Words in Classic Novels

Rachel Lea Draelos

原文摘要

This paper proposes a trustworthy computational method to gender-swap public domain novels, with programmatic guarantees that only gendered words are inverted and all other words are left unchanged. This may seem like a simple task, but both large language models and naive rule-based methods fail to complete it. The proposed method includes a custom whitespace-preserving tokenizer, gender-swap mappings across plural and possessive forms, pronoun handling, and a character renaming strategy. It shortens the time required to gender-swap a novel from weeks of manual labor to minutes. The method is demonstrated on two public-domain novels. Pride and Prejudice is transported to matriarchal Regency England, where inheritance flows through daughters, mothers rule the household, and a frantic Mr. Bennet is determined to secure advantageous marriages for his sons. The Great Gatsby pivots to a matriarchal Jazz Age where newly-rich Ms. Gatsbee throws lavish parties in the hopes of reuniting with her lost love, David, who is now married to the violent Thelma Buchanan. Gender-swapped novels could serve as useful tools to investigate the gender biases of readers, authors, societies, and AI systems.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07133

Context-Aware Automated NMR Structure Elucidation

Frank Hu, Jonathan M. Tubb, Grant M. Rotskoff, Matthew W. Kanan, Thomas E. Markland

原文摘要

Machine learning approaches are rapidly revolutionizing the ability to analyze and extract structural insights from chemical data, enabling high-throughput chemical discovery. While existing data-driven approaches have shown remarkable promise for predicting chemical structures in the de novo setting, incorporating additional chemically informed context offers the opportunity to further improve these models' performance and extend their real-world utility. Here we provide a straightforward protocol to combine artificial-intelligence-based spectroscopic elucidation pipelines with contextual chemical data, e.g., known reactants or a desired product, by using automated retrosynthetic analysis to generate large training sets of molecules and contextual data in the form of synthetic precursors. We demonstrate our approach by showing that our recently introduced NMR structure elucidation framework can be trained to effectively leverage molecular precursor context. We show that when the chemical context provides useful information on the target molecule we obtain dramatically improved accuracy over using NMR spectra alone, obtaining Top-15 accuracies of up to 95.4% for molecules up to 40 heavy atoms spanning the full elemental range of organic chemistry. Furthermore, we demonstrate that our approach is highly robust, such that even when the context provided is uncorrelated with the target, one still obtains accuracies commensurate with those of the original NMR structure elucidation framework. This approach provides an efficient route to improve a wide range of spectroscopic structure elucidation platforms using contextual data.

physics.chem-ph · cs.LG

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2601.22478

Transformation-Augmented GRPO for Enhancing Exploration in Reasoning of Large Language Models

Khiem Le, Phuc Nguyen, Youssef Mroueh, Chi-Heng Lin, Shangqian Gao, Ting Hua, Nitesh V. Chawla

原文摘要

Group Relative Policy Optimization (GRPO) has become the dominant reinforcement learning method for eliciting reasoning capabilities in LLMs using verifiable rewards. Still, it suffers from two critical issues that limit models' exploration: \textbf{Gradient vanishing} and \textbf{Diversity collapse}. When a training question is too easy or too hard, all sampled responses receive identical rewards, yielding no gradient signal. Meanwhile, the model tends to collapse its responses toward a single reasoning pattern rather than exploring diverse strategies. We propose Transformation-Augmented GRPO (TA-GRPO), a simple yet effective method that addresses both issues via question transformation. For each training question, we automatically create multiple problem-equivalent rephrased questions that alter wording, format, and information order while preserving the underlying intent. As these rephrasings shift the model's perceived difficulty, responses across the original question and its rephrasings receive mixed rewards and encode more diverse reasoning strategies within it. TA-GRPO jointly computes advantages over this expanded response group and aligns all importance sampling ratios to the original question, enabling the model to learn from a richer set of attempts. Experiments with four LLMs (Qwen3-1.7B/4B-Base and Llama-3.2-1B/3B-Instruct) show that TA-GRPO consistently improves pass@$k$ on competition-level mathematics benchmarks (AMC, OlympiadBench, AIME24, AIME25) and out-of-distribution benchmarks (Minerva, GPQA-Diamond). Notably, it improves the average pass@32 of Qwen3-1.7B-Base and Qwen3-4B-Base by \textbf{4.97}\% and \textbf{4.35}\%, respectively, and matches the pass@32 of baselines trained with up to 2.5x more data.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.13699

MIND: AI Co-Scientist for Material Research

Geonhee Ahn, Donghyun Lee, Hayoung Doo, Jonggeol Na, Hyunsoo Cho, Sookyung Kim

原文摘要

Large language models (LLMs) have enabled agentic AI systems for scientific discovery, but most approaches remain limited to textbased reasoning without automated experimental verification. We propose MIND, an LLM-driven framework for automated hypothesis validation in materials research. MIND organizes the scientific discovery process into hypothesis refinement, experimentation, and debate-based validation within a multi-agent pipeline. For experimental verification, the system integrates Machine Learning Interatomic Potentials, particularly SevenNet-Omni, enabling scalable in-silico experiments. We also provide a web-based user interface for automated hypothesis testing. The modular design allows additional experimental modules to be integrated, making the framework adaptable to broader scientific workflows. The code is available at: https://github.com/IMMS-Ewha/MIND, and a demonstration video at: https://youtu.be/lqiFe1OQzN4.

cs.MA · cs.AI · cs.CE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07127

Scalable Bayesian Gaussian Process Classification for Cancer Histopathology: Calibrated Uncertainty Under Distribution Shift

Yunru Zheng, Wun Ting Chan, Sampras Dsouza, Zhenyu Hong

原文摘要

Reliable uncertainty quantification is essential for clinical deployment of cancer diagnosis models, yet neural networks and variational Gaussian process methods can produce overconfident predictions, particularly under the distribution shifts that arise when models trained at one hospital are applied at another. We present a scalable Bayesian Gaussian Process Classification (GPC) framework that operates on frozen pathology foundation model embeddings (Phikon) and combines accelerated Randomly Pivoted Cholesky (RPCholesky) for low-rank kernel factorization, Hamiltonian Monte Carlo (HMC) with non-centered parameterization for posterior sampling, and a pivots-as-inducing-points scheme for prediction. This makes HMC posterior sampling in the low-rank latent representation tractable at N approximately 300,000 training points, enabling HC-based low-rank GPC on substantially larger datasets than prior MCMC-based demonstrations. We evaluate on two histopathology benchmarks for breast cancer metastasis detection: PCam and CAMELYON17-WILDS, where the latter tests calibration under hospital-level distribution shift. Compared to stochastic variational GP and neural network baselines, our method improves calibration and predictive performance - reducing expected calibration error by up to 88% and false negative rate by up to 90% - with the largest gains appearing under distribution shift, suggesting that HMC-based posterior sampling provides more trustworthy uncertainty estimates for cross-site pathology classification.

cs.LG

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版本更新 · v2arXiv:2603.13665

An Extended Study of Gear-Ratio-Aware Standard Cell Layout Generation for DTCO Exploration

Chung-Kuan Cheng, Andrew B. Kahng, Bill Lin, Yucheng Wang, Dooseok Yoon

原文摘要

Advanced nodes decouple contacted poly pitch (CPP) and lower-metal pitch to improve routability. We present CPCell, an efficient standard-cell layout generation framework, to support arbitrary gear ratio (GR) and offset parameters through a fine-grained layered grid graph and constraint-programming-based placement-routing co-optimization. Layout quality is improved via Middle-of-Line routing, M0 pin enablement, pin accessibility constraints and a weighted multi-objective formulation that jointly optimizes cell layouts. To scale to netlists with up to 48 transistors, we incorporate acceleration techniques including transistor clustering, identical transistor partitioning, routing lower bound tightening and early termination strategies. Comprehensive cell-level and block-level studies are conducted to evaluate GR and offset choices, quantify the benefits of the proposed objectives and assess their impact on power, performance, area and IR-drop outcomes.

cs.AR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.03056

MOF-VERIFY: A Failure-Aware Agentic Harness for MOF Hypothesis Verification

Donghyun Lee, Taehoon Lee, Geonhee Ahn, Jieun Kim, Jihyun Park, Suyeon Cho, Yoona Kim, Chaerim Shin 等 13 位作者

原文摘要

Large language models are increasingly used as reasoning components in AI-driven materials Co-Scientists, yet the reliability of the resulting verification pipeline remains unclear. Metal-organic frameworks (MOFs) provide a particularly challenging setting because structures may appear under different identifiers, synthesis outcomes depend strongly on experimental conditions, evidence is distributed across heterogeneous sources, and some hypotheses require computation rather than literature alone. We introduce a diagnostic benchmark with four task families covering structural grounding, synthesis-condition verification, evidence-sufficiency verification, and MLIP-based computational verification. T-MOF-1-3 are evaluated under closed-book, retrieval-enabled, and oracle-evidence settings to localize failures in knowledge access, evidence acquisition, and reasoning, while T-MOF-4 separately evaluates computational verification. Guided by these diagnosed failure modes, we develop MOF-Verify, a failure-aware agentic harness that targets structural, literature, evidence-sufficiency, and computational bottlenecks before producing a final verdict. Across multiple backbone LLMs, MOF-Verify improves the reported average over direct inference and retrieval-based baselines on this benchmark, with gains concentrated in structural grounding and evidence sufficiency. Benchmark records and release-status documentation are available at https://github.com/IMMS-Ewha/MOF-Verify-Benchmark.

cs.AI · cs.CE

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新论文 · v1arXiv:2611.07126

Adapting LLM Judges to Observed Human Annotation Policy through Soft Prompt Tuning

Tamás Janusko, Sven Kleinkop, Ricardo Bochnia, Gunnar Hempel, Steffen Tomschke, Jürgen Anke, Maik Thiele

原文摘要

Large Language Models (LLMs) are increasingly employed as automated judges for evaluating chatbot responses. While proprietary models can achieve high agreement with human annotators, they remain costly and difficult to deploy in practical settings. Lightweight open-source LLM judges provide an attractive alternative but typically exhibit lower agreement with human ratings. In this paper, we investigate whether soft prompt tuning can align open-source LLM judges more closely with human annotation policy. We train soft prompts on the FairBotBench benchmark and evaluate the adapted judges across the dimensions of bias, toxicity, and personalization. Our findings demonstrate that hybrid soft-prompt tuning, which combines textual prompts with learned continuous prompt embeddings, consistently improves agreement with human annotations over the corresponding untuned baselines. For Qwen 3.5 9B, the adapted judges surpass both hosted reference judges across all three evaluation dimensions.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.15391

PanoWorld: Geometry-Consistent Panoramic Video World Modeling

Le Jiang, Xiangyu Bai, Bishoy Galoaa, Shayda Moezzi, Caleb James Lee, Tooba Imtiaz, Edmund Yeh, Jennifer Dy 等 10 位作者

原文摘要

We present PanoWorld, a panoramic video world model that generates geometry-consistent 360$\degree$ video from a single image and a caption. Existing panoramic video methods optimize primarily for visual realism and do not explicitly constrain the underlying 3D scene state, producing outputs that appear plausible yet exhibit inconsistent depth, broken correspondences, and implausible motion across the spherical surface. We address this gap by framing panoramic video generation as a geometry- and dynamics-consistent latent state modeling problem rather than pure visual synthesis. Building on a pre-trained perspective video world model, we introduce two lightweight regularizers: a depth consistency loss against pseudo ground-truth panoramic depth, and a trajectory consistency loss that supervises the 3D world-frame positions of tracked points across time. We further apply spherical-geometry-aware adaptation to the conditioning and positional encoding. We additionally introduce PanoGeo, a unified geometry-aware panoramic video dataset with consistent depth, trajectory, and prompt annotations across diverse real and synthetic sources, used for both training and stratified evaluation. Experiments show that PanoWorld improves geometric consistency over prior panoramic generation methods while maintaining competitive visual realism, establishing that panoramic video generation must be treated as a geometric modeling problem to support the holistic spatial understanding requirements of embodied AI applications. Code is available at https://github.com/ostadabbas/PanoWorld.

cs.CV · cs.AI

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版本更新 · v2arXiv:2605.06458

Invariant Features in Language Models: Geometric Characterization and Model Attribution

Agnibh Dasgupta, Abdullah Tanvir, Xin Zhong

原文摘要

Language models exhibit strong robustness to paraphrasing, suggesting that semantic information may be encoded through stable internal representations, yet the structure and origin of such invariance remain unclear. We propose a local geometric framework in which semantically equivalent inputs produce representations that occupy structured regions in latent space, with paraphrastic variation along nuisance directions and semantic identity preserved in invariant subspaces. Building on this view, we make three contributions: (1) a geometric characterization of invariant latent features, (2) a contrastive subspace discovery method that separates semantic-changing from semantic-preserving variation, and (3) an application of invariant representations to zero-shot model attribution. Across models and layers, empirical results support these contributions. Invariant structure emerges in specific depth regions, semantic displacement lies largely outside the nuisance subspace, and representation-level interventions indicate a causal role of invariant components in model outputs. Invariant representations also capture model-specific geometric patterns, enabling accurate attribution. Together, these findings support a local geometric characterization of semantic invariance in language model representations.

cs.LG · cs.CL

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新论文 · v1arXiv:2611.07125

Data-Driven Modeling of Molten Chloride Corrosion Using Static Test Data

Md Mahmudul Hasan Pathik

原文摘要

Molten chloride corrosion has been studied for decades, yet reliable prediction across different alloys, salts, and test conditions remains difficult. In this work, 291 static molten chloride corrosion experiments reported in the literature were compiled into a structured dataset and used to develop an interpretable machine-learning framework for corrosion-rate predictions. To better represent the differences in salt condition across studies, a Chloride Salt Preparation Index (CSPI) was introduced to bring the reported salt-treatment method, atmosphere control, and pre-exposure impurity condition on a common numerical scale. A blended tree-based ensemble provided the most consistent predictive performance, achieving a cross-validated \(R^2\) of 0.85 and an MAE of 0.41 mg cm\(^{-2}\) day\(^{-1}\). The graded CSPI retained more useful information than a simple purified/unpurified classification and emerged as the dominant model descriptor. Global sensitivity and SHAP analyses further identified exposure time, temperature, and alloy chemistry as important contributors to the model predictions, with several observed trends consistent with the established molten-chloride corrosion behavior. The conformal prediction intervals were well calibrated, providing an estimate of prediction uncertainty in addition to the point prediction. Overall, the framework demonstrates that historical corrosion data can support quantitative, interpretable, and uncertainty-aware prediction when experimental context is represented explicitly, while also highlighting where more consistent reporting and targeted experiments are needed to improve future molten-salt corrosion models.

cs.LG · cond-mat.mtrl-sci

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新论文 · v1arXiv:2611.07124

Machine Learning for Live-Birth Prediction After IVF/ICSI From Structured Clinical Data: A Systematic Review, Meta-Analysis, and Critical Appraisal

Abdul Raheem Shahzad, Muhammad Zaheer Khan, Umar Ajaib, Asad Ur Rehman, Juan Deng, Ming Zhang

原文摘要

Machine-learning models predicting live birth after in vitro fertilisation (IVF) or intracytoplasmic sperm injection (ICSI) from routine clinical data report performance from near chance to near perfect. We systematically reviewed these models under PRISMA 2020, searching OpenAlex, Scopus and Web of Science to 22 October 2026, appraised them with PROBAST+AI and TRIPOD+AI, and pooled their discrimination with random-effects meta-analysis on the logit scale. We define what fixes the meaning of a model's output (decision point, endpoint, unit of analysis, data source) and map the 48 included studies by model family, decision point and validation design. Models evaluated only internally pooled to an area under the receiver operating characteristic curve (AUC) of 0.814, against 0.756 (95% prediction interval 0.546-0.889) for models evaluated on a later period or another centre, and 0.764 for studies not at high risk of bias; neither difference was significant, and heterogeneity was extreme. Models whose decision point was not stated pooled to 0.910, the only significant moderator in meta-regression. Against logistic regression fitted to the same data, the best machine-learning model gained a pooled 0.056 AUC (95% CI 0.017-0.090). Of 48 studies, 47 were at high or unclear risk of bias, only four assessed the calibration slope, and no model has evidence of clinical impact. The certainty of the pooled evidence is low to very low; the gains that survive validation came from staging predictions to decision points and local calibration, not deeper architectures. We provide a TRIPOD+AI-mapped checklist, a benchmark specification and a research agenda.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07123

PromptOps: Iterative Prompt Optimization for LLM-as-a-Judge Systems

Sven Kleinkop, Tamás Janusko, Ricardo Bochnia, Gunnar Hempel, Steffen Tomschke, Jürgen Anke, Maik Thiele

原文摘要

Large language models (LLMs) are increasingly used as automated judges for evaluating the behavior of chatbots and other AI systems. In deployment, however, their agreement with the intended human evaluation policy may degrade as user interactions and score distributions change. Existing automatic prompt optimization methods are primarily studied as one-shot procedures on static development sets and therefore provide limited guidance for maintaining LLM judges over time. We frame this problem as judge drift and introduce PromptOps, a closedloop framework for monitoring and iteratively recalibrating LLM-as-a-Judge (LLMaaJ) systems. PromptOps separates four operational decisions: when to optimize, which optimization procedure to apply, which feedback records to use, and how to update the judge prompt. We instantiate the framework with five prompt optimization methods: GEPA, SIMBA, Rubric Synthesis, and exemplar selection using either simulated annealing or a Tree-structured Parzen Estimator. Our experiments demonstrate that iterative prompt optimization can recalibrate LLM judges and recover performance after controlled shifts in the referencescore distribution. However, effective maintenance is inherently contextdependent: neither optimization algorithm choice nor update timing generalizes universally across settings. These findings establish prompt optimization for deployed LLM judges as an inherently iterative maintenance problem and demonstrate that PromptOps provides a practical abstraction for studying and implementing this process.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.00878

Heraclitean Dialectical Concept Space

Volkan Yildiz

原文摘要

We introduce Heraclitean Dialectical Concept Spaces (HDCS), a topological framework for modelling stagewise structural change in systems of concepts. A stage consists of a nonempty concept set, a feasible family generating an external topology, and a neighbourhood assignment carrying additional relational structure. Neighbourhood overlap induces adjacency and channel ideals. Candidate emergence is represented by the CCER construction: an interaction profile, finite admissible cores, and anchored witnesses determine nonempty open candidate regions in uncovered remainder structure. Selected candidates may be reified as new atomic concepts at a later stage, while partial injective carry maps separately track persistence of existing identities. The resulting history space is obtained as a quotient of the disjoint union of stage spaces with the corresponding final topology. Product concept spaces extend the construction to cross-domain interaction. We derive support characterizations, mediated-interaction components, canonical CCER envelopes, monotonicity and impossibility criteria, product-space factorization, and invariance under concept-space isomorphism. Applications to monetary transformation, zero and representation, mammalian middle-ear evolution, and AI-related labour change illustrate the framework. HDCS is intended as a structural formalism rather than a causal or predictive theory of historical change.

physics.soc-ph · cs.LO · math.GN

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