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Product Hunt ·

Shedi

Get back the space Xcode, Docker and node_modules ate

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Koi

Every tool you need, in one Mac app

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Agent Pets

Coding agents as pets that live on every page in Chrome

V2EX ·

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1024midalo 米搭给大家发福利啦！： 🎁 新用户注册进群即送 5$ 额度 站点地址： https://model.midalo.ai/ 官方 QQ 群：389039568 （注册后进群，私信管理员或联系客服发送账户 ID 即可领取 5$，24 小时内到账） 客服 QQ：480419379 站点介绍 1. 模型覆盖全 覆盖 Claude / GPT / Gemini / DeepSeek / Grok / GLM / Kimi(含 k3)，共 57+ 个模型。模型广场可实时查看当前可用型号和价格，新模型上线后第一时间跟进。 2. 计费清晰 支付宝 / 微信 / USDT 实际扣费以控制台使用日志为准。 3. 开票相关 是否支持开票：支持 多少起开：一般纳税人 6 点 5000 ，小规模 1 点 1000 是否有手续费：3% 票据类型（个人开票/企业开票）：开票给企业 开票主体是否为科技类公司：是 1：1 充值，欢迎各位 V 友！

arXiv ·

H2DF: Calibrated Target Bands for Controllable Language-Model Unlearning

arXiv:2610.26864v1 Announce Type: new Abstract: Gradient ascent (GA) is a simple language-model unlearning baseline, but it continues increasing forget loss after useful forgetting has occurred. This unbounded pressure can damage retained utility and make forgotten examples abnormally high-loss. We introduce H2DF, a calibrated target-band objective that applies GA-like pressure below a data-dependent lower target and suppresses, or reverses, that pressure after the target region is reached. The

arXiv ·

AutoTerm-SST: Adaptive Terminology Memory with Zero Session-Time Setup for Streaming Speech Translation

arXiv:2610.26865v1 Announce Type: new Abstract: Retrieval-augmented speech translation improves rare terminology, but systems such as RASST still assume that users provide a glossary before each session. That assumption is inconvenient for live talks, while merging every domain into one growing glossary eventually places too many irrelevant terms in the same retrieval and prompt competition. We present AutoTerm-SST, an interactive system that removes session-time glossary upload and domain selec

arXiv ·

WaveCommitments: Private and Verifiable Speech-Agent Marketplaces

arXiv:2610.26867v2 Announce Type: new Abstract: Modern speech systems are evolving into ecosystems of autonomous speech agents that interact, negotiate, and trade assets such as embeddings, spectrograms, or raw data. In such markets, sellers cannot reveal assets for evaluation due to privacy constraints, and buyers cannot price what they cannot test. Thus, we introduce WaveCommitments, an end-to-end design that couples multivariate polynomial commitments with confidential asset valuation via Dat

arXiv ·

Multimodal Ambivalence and Hesitancy Recognition with Bidirectional Audio--Text Cross-Attention

arXiv:2610.26868v1 Announce Type: new Abstract: Ambivalence and hesitancy (A/H) are subtle behavioural states that can be expressed through both what a person says and how it is spoken. This paper presents an audio--text framework for video-level A/H recognition. Full-video transcripts and speech waveforms are encoded using pre-trained multilingual BERT and wav2vec~2.0, respectively, and projected into a shared representation space. Two stacked bidirectional cross-attention blocks allow linguist

arXiv ·

Mixture of Phonetic Experts Based Low-Rank Adaptation of Conformer Models for Accented English Speech Recognition

arXiv:2610.26888v1 Announce Type: new Abstract: We present Mixture of Phonetic Experts based Low-Rank Adaptation (MoPE-LoRA), a parameter-efficient adaptation framework for accented English speech recognition. Recent mixture-of-experts approaches typically allocate experts per accent, implicitly treating accent identity as the primary axis of variation. In contrast, we organize adaptation along phonetic categories, motivated by the observation that accent differences are often expressed as syste

arXiv ·

AtomSD: Procedural Atom-Guided Self-Distillation for LLM Agents

arXiv:2610.26926v1 Announce Type: new Abstract: Large language models (LLMs) possess broad factual knowledge and strong reasoning capabilities, but solving complex tasks also requires procedural knowledge about how to act. Existing approaches often represent procedural knowledge through trajectories or bundled skills, limiting flexible reuse across interaction histories. This paper introduces Procedural Atoms, fine-grained and independently applicable units that represent Intent, When, Do, and A

arXiv ·

Do We Need the Largest LLMs for Every Clinical Decision?

arXiv:2610.26937v1 Announce Type: new Abstract: Large clinical language models often require multi-GPU infrastructure, limiting local deployment. We introduce CERTA, a decision-level statistical certificate that determines when a quantized model may substitute for its native BF16 reference and escalates decisions that cannot be certified to it. Across eleven instruction-tuned models, both general-purpose and domain-adapted, from three families (7-72B), we evaluated 2,500 clinical-verification de

arXiv ·

The Bottleneck Is Retrieval, Not Reading: Diagnosing Evidence-Based Generation with RAG for Long Context Question Answering

arXiv:2610.26951v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is the dominant paradigm for grounding large language models in external corpora, but its similarity-driven, chunked pipeline has structural blind spots: it cannot anticipate which passages a question requires and discards document-level context that resolves cross-passage references. We introduce evidence-based generation (EBG), requiring models to return an answer with line-level citations, and a joint Ans+Evi

arXiv ·

A Multi-granularity Interaction Network for Relational Triple Extraction

arXiv:2610.26952v1 Announce Type: new Abstract: Relational triple extraction identifies entity relations from raw sentences without depending on entity type information. The performance of traditional methods is seriously degenerated by ``unseen'' entities, because these methods can't build contextual dependencies relevant ``unseen'' entities. This paper addresses this challenge by proposing a multi-granularity interaction network that leverages word-word, entity-relation, and head-tail interact

arXiv ·

LAMB: Latent Multi-Hypothesis Beliefs for Long-Horizon Large Language Agents

arXiv:2610.26955v1 Announce Type: new Abstract: Long-horizon language agents require compact memories that retain interaction evidence while preserving competing hypotheses about unobserved states. Although effective, many belief-state methods encode memory in discrete natural language, where repeated compression can omit evidence or prematurely discard plausible alternatives. We introduce LAMB, a recurrent framework for latent multi-hypothesis beliefs. A Common--Delta factorization combines a s

arXiv ·

SZED at ArGuard Shared Tasks: Multi-Task Label-Attention and Cross-Modal Fusion for Arabic Hateful Meme Detection

arXiv:2610.26972v1 Announce Type: new Abstract: We present SZED's submission to ArGuard 2026 Track A: Arabic hateful meme detection. The task has two parts: binary classification (A1) and fine-grained multi-label tagging (A2). For A1, we fuse MARBERTv2 and CLIP ViT-L/14 embeddings, comparing concatenation, self-attention, and ensemble heads. For A2, we use a multi-task label-attention architecture with per-class query vectors and an auxiliary head for cascade calibration. We reach macro-F1 = 0.7

arXiv ·

Sharp Degree Bounds for Putinar Certificates in Terms of the Positivity Margin via Analytic Sums of Squares

arXiv:2610.26976v1 Announce Type: new Abstract: We establish sharp degree bounds for Putinar sum-of-squares (SOS) certificates in terms of the positivity margin $\delta$. For any fixed Archimedean quadratic module with prescribed generators and any fixed bound on the degree of the objective, we prove a uniform certificate-degree upper bound of $O(\delta^{-1/2}\log^2(2/\delta))$ for normalized polynomial objectives. We show that this bound is sharp by constructing a matching lower-bound example i

arXiv ·

Think Before You Transcribe: Context-Steered Reasoning for Hotword-Aware Speech Recognition

arXiv:2610.26986v1 Announce Type: new Abstract: Contextual automatic speech recognition (ASR) improves rare-entity recognition by supplying candidate hotwords, but retrieved candidates may be incomplete or incorrect. A reliable system must therefore exploit a relevant candidate without allowing unsupported alternatives to override the acoustic evidence. We propose a context-steered reasoning framework that generates a short, audio-grounded caption before transcription. This task-specific represe

arXiv ·

TRACE: Training-aware Robustness and Challenge Evaluation for Text-Vision Embedding Models

arXiv:2610.26993v1 Announce Type: new Abstract: Existing benchmarks for text-vision embedding models report aggregate scores that obscure whether performance comes from general capabilities, task-specific adaptation, or robustness to challenging tasks. We introduce TRACE, a Training-aware Robustness and Challenge Evaluation framework that reorganizes 168 tasks from various sources of datasets into three categories: General, Adaptability, and Challenge. Through controlled experiments on two text-

arXiv ·

How to get the best and fairest performance out of your ASR system using activation steering

arXiv:2610.26994v1 Announce Type: new Abstract: Activation steering for ASR systems is an underexplored technique in improving both ASR performance and demographic fairness. We investigate two forms of activation steering: paired activation subtraction, and supervised optimization. We find the former to be ineffective at ameliorating ASR performance whatsoever, while the latter is quite effective at deriving steering vectors (SVs) that improve at once overall ASR performance and demographic fair