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SemiEngineering

AI Image Enhancement Improves Detection Of Flip-Chip Interconnect Defects (MCL, PVA TePla, ASE)

Researchers at Materials Center Leoben, PVA TePla, and Advanced Semiconductor Engineering (ASE) published “AI-Based Image Enhancement Improves Detection of Interconnect Defects in Microelectronic Packaging.” Abstract Excerpt: “Traditional failure-analysis (FA) methods face growing challenges due to the increasing miniaturization and complexity of modern semiconductor devices. In particular, the detection of cracks in the extreme low K... » read more The post AI Image Enhancement Improves Detection Of Flip-Chip Interconnect Defects (MCL, PVA TePla, ASE) appeared first on Semiconductor Engineering.

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SemiEngineering

3D Integration Reduces Embodied-Emissions Impact Of GPU Scaling (imec, VUB)

Researchers at imec and Vrije Universiteit Brussels published “Embodied Emissions of 2.5D and 3D Scaling in Chipletized GPUs for Low-Carbon AI.” Abstract “GPU compute resources are scaling to support growing AI workloads, leading to further rises in the climate change impact of hyperscale data centers that rely on high-performance GPUs. To model how packaging decisions... » read more The post 3D Integration Reduces Embodied-Emissions Impact Of GPU Scaling (imec, VUB) appeared first on Semiconductor Engineering.

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Titan Media

AI-written code: Too fast for humans to review—NVIDIA optimizes Kimi K3 with 12,000 lines of code, opening a new battleground?

Most reports focus only on Kimi K3’s 1.68x speed boost, overlooking a key warning from the vLLM blog: AI-generated inference code is now so fast that humans can’t keep up with line-by-line scrutiny. NVIDIA engineers leveraged AI to write a 12,000-line Megakernel, squeezing maximum performance out of the model without upgrading hardware. The center of gravity in the compute power race has shifted toward software stacks, and the human-in-the-loop paradigm is being redefined.

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arXiv cs.CV

Intersectional Invariant Learning for Fair Deepfake Detection

arXiv:2610.37881v1 Announce Type: new Abstract: Deepfake detection models achieve high overall accuracy but exhibit large performance disparities across demographic groups, causing some groups to experience substantially higher false-positive rates than others. Existing fairness-aware methods address this by constraining model outputs to equalize group predictions, but do not tackle the underlying feature-level bias. As a result, models may still rely on demographic characteristics, such as skin texture, as indirect cues for detecting manipulated content. To address this problem, we propose Intersectional Invariant Learning (IIL), a framework that improves feature learning rather than only adjusting model outputs. IIL alternates between identifying the most challenging demographic partitio

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arXiv cs.CV

Convex Potential Learning for Optimal Transport Between Medical Images

arXiv:2610.37844v1 Announce Type: new Abstract: Computing optimal transport maps between medical images plays a fundamental role in morphological analysis. However, existing methods are either difficult to scale to large 3D medical images or rely on regularized formulations that provide only approximate transport maps. To improve the accuracy of downstream medical image analysis, we propose a novel approach for estimating optimal transport maps by parameterizing the convex potential function. By construction, the resulting transport map is guaranteed to be curl-free, consistent with Brenier's theorem. We evaluate the proposed method on both 2D and 3D medical imaging datasets. Experimental results demonstrate that our approach achieves lower transport estimation errors and produces higher-q

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arXiv cs.AI

Amortized Localized Posterior Inference for Model Calibration with Application to an Ocean Turbulence Closure

arXiv:2610.37731v1 Announce Type: new Abstract: Model discrepancy may prevent a single calibration parameter from reproducing a physical response across the context domain. In addition, uncertainties in observations and contexts affect calibration and must be accounted for and quantified. We construct a family of localized posteriors by weighting observation likelihoods according to their proximity to a target context. Gaussian localization weights incorporate context uncertainty through covariance inflation, with reflection at domain boundaries and an optional correction for uneven context sampling. A conditional normalizing flow approximates the family of posteriors through amortized variational inference. Integrating the localized posteriors under a prescribed context measure gives the

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arXiv cs.AI

What Constitutes Food Similarity? Visual Representations and Retrieval in Indian Cuisine

arXiv:2610.37867v1 Announce Type: new Abstract: Food similarity underlies retrieval, recommendation, substitution, and other food computing tasks, yet food representations are typically evaluated by identity preservation. We ask what happens to food similarity beyond identity: how different visual representations organize foods into relational neighborhoods, what culinary relationships these neighborhoods reveal or obscure, and how consistently these structures emerge across representations. Indian cuisine provides a challenging setting in which visually similar foods may have different culinary identities, while foods within a culinary family can vary substantially in appearance. Using an expanded Khana dataset and seven pretrained visual representations spanning convolutional, transforme

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arXiv cs.AI

FilingLoop: Evaluating and Evolving LLM Agents for Corporate Disclosure Review and Revision

arXiv:2610.37905v1 Announce Type: new Abstract: Public disclosure repositories such as SEC EDGAR support informed investment through web access to corporate filings. Supporting their review with language model agents requires discovering unstated deficiencies across related documents, acquiring missing company facts, and verifying that requested revisions satisfy evidence-backed requirements. Existing financial question-answering evaluations provide limited evidence of these interactive capabilities. We introduce \textbf{FilingLoop}, an interactive benchmark and a self-evolving agent framework for corporate disclosure review and revision. Grounded in historical U.S.\ Securities and Exchange Commission correspondence, FilingLoop reconstructs 380 predominantly IPO review tasks from 96 compan

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arXiv cs.LG

Spokes, not a Ring: Revisiting the Weekday Manifold in Language Models

arXiv:2610.37796v1 Announce Type: new Abstract: Recent work has hypothesised that large language models may represent some concepts as multi-dimensional manifolds instead of single directions. Using these manifolds to interpret or steer representations often assumes that moving along them corresponds to a continuous change in the represented concept. In this work, we test this continuous manifold assumption on the prototypical weekday manifold. Despite its striking circular geometry, we find that the weekday representations behave more like seven discrete states arranged in a circle than a continuous ring. First, we show that local sensitivity has seven minima aligned with the weekdays, producing a step-like mapping between representation and behaviour under on-manifold steering. Second, w

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arXiv cs.CL

Rank-Aware Differentially Private Release of Listwise Preferences for LLM Alignment

arXiv:2610.37682v1 Announce Type: new Abstract: Listwise preference feedback offers richer supervision for large language model (LLMs) alignment than pairwise comparisons, but a full ranking also reveals more sensitive user preferences. Existing privacy-preserving alignment methods focus mainly on pairwise feedback, while listwise alignment under local differential privacy (LDP) remains unexplored. Extending LDP to this listwise domain raises a critical challenge: the high sensitivity of the full ranking space demands excessive noise, neutralizing the benefits of listwise supervision. To address this challenge, we analyze the interaction between supervision granularity and privacy noise in downstream training, and introduce a rank-aware exponential mechanism that privatizes listwise prefer

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arXiv cs.DC

A Comparative Study of Parallel Implementations of Short-Time Fourier Transform

arXiv:2610.38069v1 Announce Type: new Abstract: The short-time Fourier transform (STFT) computes the Fourier transform of a short-duration window of a longer signal, which characterizes how a frequency changes over time. While the Fourier transform algorithms, particularly the Fast Fourier Transform (FFT) algorithm, has been well developed to take advantage of parallel computing, there is limited work on understanding the performance of parallel short-time Fourier transform algorithms. In this study, two parallel short-time Fourier transform algorithms, one is FFT-based and the other a quasi-parallel feedforward algorithm, are implemented and analyzed in comparison with each other. It is found that while the quasi-parallel algorithm outperforms in runtime under low concurrency settings, th

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arXiv cs.CL

Exposure Is Not Commitment: Double-Stratified Evaluation of Latent Monitors for Tool-Using Agents

arXiv:2610.37679v1 Announce Type: new Abstract: Tool-using language agents are vulnerable to indirect prompt injection, and a growing literature probes model internals to catch unsafe actions before they execute, with sharply conflicting results. We argue much of the conflict is an evaluation artifact, and propose a double-stratification protocol: separate injection exposure from behavioral commitment, in both the evaluation and the training population. On the primary Qwen AgentDojo grid, monitors trained on the naive mixed unsafe-versus-rest contrast mostly learn exposure: they flag exposed trajectories whether or not the agent complies, and barely predict which cases turn unsafe. Retrained on exposed cases only, the same probe families predict the model's own unsafe tool call up to two s

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arXiv cs.CL

Artifact Distillation: Training-Free Transfer from Frontier to Small Language Models

arXiv:2610.37680v1 Announce Type: new Abstract: Closing the gap between frontier and smaller language models is usually approached by distillation: fine-tuning the small model (student) on a frontier model's (teacher) outputs, which binds transfer to training compute and leaves the transferred knowledge largely opaque. We propose Artifact Distillation: a teacher solves training instances and externalizes its problem-solving procedure into an artifact bank that a student queries at inference time, with no gradient updates. Across four LiveBench tasks, a bank written by Claude Opus 4.5 lets Claude Haiku 4.5 outperform unaided Claude Sonnet 4.5 on spatial reasoning (0.707 vs. 0.647) at 60% of its cost, and reach Sonnet 4.5's level on table joining at 89% of its cost. The same raw bank raises

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arXiv cs.CL

Locating Sycophancy Across Training Stages in Open Language Models

arXiv:2610.37788v1 Announce Type: new Abstract: Language model assistants sometimes change a correct answer when the user suggests a wrong one. The behavior, called sycophancy, is usually attributed to preference-based post-training, yet early measurements found it in pretrained models. We ask where it comes from by measuring it at every available stage of training: 13 open checkpoints from 5 lineages, from base model through supervised fine-tuning and preference optimization to release. We separate how far an injected claim moves a model's answer from how firmly the answer was held, and vary who makes the claim, how reliable they are said to be, and how strongly they make it. Two sycophancy behaviors with different origins emerge, and both replicate across datasets. Deference to a claim i

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arXiv cs.LG

Responsible Quantum Machine Learning for Medical Imaging: A Fairness Audit Across Sensitive Subgroups

arXiv:2610.37819v1 Announce Type: new Abstract: Quantum machine learning (QML) is increasingly explored for medical classification, yet its behaviour under subgroup-aware fairness criteria remains understudied. We present an empirical fairness audit of classical and quantum classifiers for medical imaging, assuming QML is not inherently fair. Two scenarios are evaluated: dermatological lesions on DDI (sensitive attribute: Fitzpatrick skin type) and chest X-rays on CheXpert-derived data (sensitive attribute: age group). Using ResNet-18 embeddings, we train SVM, MLP, QSVM and QCNN under a baseline and three mitigation settings: differentiable Equalized Odds (EO) regularisation, Gradient Reversal Learning (GRL), and both combined. Foundation models (PanDerm, EVA-X) serve as external reference

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arXiv cs.AI

Beyond Low-Pass Dynamics: Frequency-Selective Spiking Reservoirs with Resonant Neurons

arXiv:2610.37906v1 Announce Type: new Abstract: Spiking reservoir computing combines the training efficiency of fixed recurrent reservoirs with the energy efficiency of event-driven inference, where computation is triggered by sparse spikes rather than executed densely at every timestep. However, existing spiking reservoirs almost exclusively rely on Leaky Integrate-and-Fire (LIF) neurons, whose low-pass dynamics limit their ability to distinguish information encoded at different temporal frequencies. We introduce HRF-Res, a liquid state machine in which LIF neurons are replaced by a heterogeneous population of Harmonic Resonate-and-Fire (HRF) neurons. These neurons exhibit intrinsic oscillatory dynamics and selectively respond to input frequencies, enabling the reservoir to capture freque

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arXiv cs.AI

Evaluating and improving AI agents for sequencing protocol understanding

arXiv:2610.37851v1 Announce Type: new Abstract: Existing scientific-agent benchmarks rarely evaluate whether agents can reconstruct the exact molecular structures and generation workflows specified by sequencing protocols, a capability critical for analyzing customized sequencing assays. We introduce LibStructBench, a document-grounded benchmark for reconstructing sequencing-library structures and molecular workflows. Our experiments show that frontier agents often produce approximately correct reconstructions but remain unreliable at exact reconstruction, particularly in molecular-state representation and workflow connectivity. Switching the underlying language model produced larger average performance differences than switching agent harnesses, with a statistically significant difference

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arXiv cs.DC

DistOptimize: Automatic Optimization of Distributed Protocols

arXiv:2610.39017v1 Announce Type: new Abstract: Distributed consensus and consistency protocols are difficult to reason about yet foundational to the cloud; as such, they often undergo a rigorous proof and formal verification process before deployment. The same level of rigor is rarely applied to protocol modifications, which occur frequently in production and threaten to compromise safety. In this paper, we eliminate the risk of safety violations from manual modifications by presenting an automatic optimizer for distributed protocols: DistOptimize. DistOptimize adapts throughput optimizations from academic languages to frameworks used in production, verifying the correctness of the rewrites in Rocq and automating their application through a combination of calibration and ILP-based search

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arXiv cs.CL

Supervised Fine-Tuning for Reasoning Large Language Models: A Survey

arXiv:2610.37681v1 Announce Type: new Abstract: Large reasoning models have recently demonstrated remarkable capabilities in solving complex tasks, where supervised fine-tuning (SFT) on long chain-of-thought data serves as a crucial foundation for eliciting and enhancing their reasoning abilities. Despite rapid progress in both improving and analyzing reasoning-oriented SFT, the field still lacks a systematic survey that consolidates its fast-growing literature. To fill this gap, we present a comprehensive review of recent advancements in reasoning SFT, examining the literature through the dual lenses of methodological design and analytical investigation. First, we review methodological improvements across the SFT pipeline and categorize them into data-centric approaches and algorithm-cent

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