Researchers tested top image editing models on Hugging Face and found they could easily create explicit deepfakes—and 1,000 image editing prompts show how people use the software.
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arXiv:2607.22552v1 Announce Type: new Abstract: The automatic translation of mathematical expressions in scientific literature into executable symbolic code (a process we refer to as Formula Formalization) is hindered by a severe scarcity of high-quality, ground-truth datasets specialized for technical scientific domains. In this paper, we present MioFFAn, an open-source, document-centric, and customizable framework designed to facilitate rapid annotation for this task. Building upon the MioGatt
arXiv:2607.22553v1 Announce Type: new Abstract: Peer review is an essential process in scientific research, yet the growing workload has made its automation increasingly necessary. In this study, we analyze how different types of reviewer guidelines, such as official conference guidelines and reviewer-imitating ones generated from high-quality human reviews using LLMs, affect automated peer review. Our experiments show that official conference guidelines produce review results most consistent wi
arXiv:2607.22622v1 Announce Type: new Abstract: Recent Text-to-SQL methods rely heavily on reasoning-centric paradigms such as Chain-of-Thought (CoT), achieving substantial gains on complex benchmarks at the cost of high inference-time overhead. However, a large fraction of real-world queries are simple lookups or aggregations that can be resolved without multi-step deduction, making forced reasoning wasteful. Thus, we propose AutoThinkSQL, a framework that integrates an auto-thinking mechanism
arXiv:2607.22657v1 Announce Type: new Abstract: Large language models (LLMs) can reproduce disinformation-aligned narrative frames as plausible explanations, raising the question of whether existing machine-unlearning algorithms can suppress this behavior. We introduce Level-based Evaluation of Narrative Suppression (LENS), a contextualization based evaluation protocol for testing target narrative reproduction across direct, attributed, contrastive, and abstract resistance levels. We evaluate tw
arXiv:2607.22884v1 Announce Type: new Abstract: We propose CHiPS, a lightweight character-level authorship attribution method for Romanian texts. All reported experiments are closed-set: the true author is one of the candidate authors in the training data. CHiPS studies two complementary fingerprints of writing style: CH-SVM, a character-histogram classifier based on one-character marginal distributions, and FFT12-LR, a positional-signal classifier that represents selected characters and punctua
arXiv:2607.22923v1 Announce Type: new Abstract: Cross-lingual mismatch remains a key source of overall degradation in modern speaker verification. The TidyVoice2026 Challenge targets this setting with text-independent verification, comprising 3,666 training and 808 development speakers in 40 languages and 2,200 evaluation speakers in 38 unseen languages, without language labels at test time. Starting from the official SimAM-ResNet34 baseline pretrained on VoxBlink2 and VoxCeleb2 and fine-tuned o
arXiv:2607.22925v1 Announce Type: new Abstract: A key question for AI safety is whether a language model expresses all of its reasoning in its output tokens. We demonstrate a concrete failure mode where frontier models exhibit invisible reasoning by leveraging semantically irrelevant filler tokens to improve performance on synthetic reasoning tasks. We evaluate 13 frontier language models across three tasks and find that many models benefit significantly from filler tokens, with accuracy improve
arXiv:2607.22954v1 Announce Type: new Abstract: Objective: To characterize the kinds of internal documentation inconsistencies a general-domain large language model (LLM) can surface from real-world discharge summaries, and to identify recurring failure modes that limit reliability at scale. Materials and Methods: We applied a two-stage LLM pipeline---open-ended candidate identification (Gemini 2.5 Pro) followed by context-grounded verification (Gemini 2.5 Flash)---to 3,000 randomly sampled MIMI
arXiv:2607.22996v1 Announce Type: new Abstract: Large language models (LLMs) deployed in educational settings often behave as direct answerers: they disclose target concepts in the opening turn instead of guiding students through progressive inquiry, as Socratic pedagogy prescribes. We present HeuristicEdu, a two-phase pipeline that aligns Qwen2.5-7B toward Socratic tutoring via supervised warm-up and Group Relative Policy Optimization (GRPO). Training uses SocraticEdu, 797 multi-turn Chinese ch
arXiv:2607.23037v1 Announce Type: new Abstract: Large language models (LLMs) can predict interpersonal attraction from conversation transcripts, but it remains unclear what a speech predictor can add beyond transcript-only LLM prediction. Using Japanese speed-dating conversations, we combine predictions from a transcript-only LLM and a supervised speech predictor to estimate participants' reported liking of their partners. We show that speech can complement transcript-only LLM prediction, but th
arXiv:2607.23058v1 Announce Type: new Abstract: Multilingual reasoning evaluation overwhelmingly relies on translating English benchmarks, a practice that introduces linguistic artifacts and fails to test culturally-grounded reasoning. We introduce ADAGE (Analogical Difficulty-by-design Assessment for Grounded Evaluation), a language-agnostic pipeline that combines native-speaker curation with LLM-assisted generation to construct challenging, translation-free benchmarks for abstract analogical r
arXiv:2607.23067v1 Announce Type: new Abstract: Contrastive decoding methods such as DoLa improve the factuality of Large Language Models (LLMs) by contrasting the output distributions of mature and premature layers. However, DoLa's dynamic layer selection relies solely on divergences in output vocabulary distributions. In this work, we propose three attention-guided strategies: Attention-JSD, Attention-Entropy-Max, and Attention-Entropy-Min, which leverage structural information carried by inte
arXiv:2607.23083v1 Announce Type: new Abstract: Gender-inclusive language generation seeks to transform biased text into inclusive alternatives while preserving semantic meaning and contextual coherence. This paper presents the IHLC system for the LT-EDI 2026 Shared Task, addressing both gender-inclusive rewriting and counter-narrative generation. For gender-inclusive rewriting, we employ parameter-efficient Low-Rank Adaptation (LoRA) fine-tuning, achieving an official score of 80.00%. Our prima
arXiv:2607.23175v1 Announce Type: new Abstract: Reducing toxicity is often framed as a global alignment problem, yet perceptions of harmful language are subjective and context-dependent. We present the first comparative evaluation of training-free methods for aligning language generation to user-specific toxicity sensitivities across three inference-time intervention stages: pre-decoding (prompt conditioning and rewriting), in-decoding (token, logit, and representation steering), and post-decodi
arXiv:2607.23242v1 Announce Type: new Abstract: Large Language Models (LLMs) have transformed conversational AI, yet high-quality multilingual code-mixed dialogue resources remain scarce, particularly for Indic languages where speakers naturally alternate between English and their native language in both native-script and Romanized forms. We present IndicTalk, one of the largest multilingual Indic code-mixed conversational corpora, comprising over 13,28,604 event-grounded multi-turn conversation
arXiv:2607.23278v1 Announce Type: new Abstract: Multi-hop question answering requires coordinating relational and textual evidence across reasoning steps, a combination neither a text corpus nor a knowledge graph can supply alone. Prior work often emphasizes only part of this loop: graph-augmented RAG retrieves from a pre-built or query-updated graph, KGQA systems search within topic-centered subgraphs, and memory-augmented agents maintain evolving memories without continuously reconciling graph
arXiv:2607.23319v1 Announce Type: new Abstract: Standard subword tokenization algorithms such as Byte-Pair Encoding (BPE) and SentencePiece are trained predominantly on modern language corpora and produce inefficient segmentations when applied to classical Indian languages. Sanskrit, Tamil, and other classical Indic languages exhibit agglutinative morphology, productive sandhi (phonological fusion at word boundaries), and domain-specific vocabularies absent from general-purpose training data. Th
arXiv:2607.23322v1 Announce Type: new Abstract: Instruction tuning has become the standard method for adapting large language models to follow human intent, yet existing instruction datasets are dominated by English-language general-knowledge tasks and lack coverage of specialized pedagogical domains. This paper presents IKS-Instruct, a dataset of 24,795 instruction-response pairs for teaching language models to deliver educational content grounded in Indian Knowledge Systems (IKS). The dataset
arXiv:2607.23344v1 Announce Type: new Abstract: Named Entity Recognition (NER) for low-resource languages such as Marathi remains a challenging task due to limited annotated resources and linguistic complexity. Although recent Large Language Models (LLMs) have demonstrated strong performance across a wide range of natural language processing tasks, their effectiveness for language-specific NER in low-resource settings remains uncertain. In this study, we fine-tune MahaBERT-v2 on different varian
arXiv:2607.23362v1 Announce Type: new Abstract: Recent work has shown that subword vocabularies can be trained to optimize compression for a specific inference rule rather than relying on greedy heuristics such as Byte Pair Encoding (BPE). We extend this approach to greedy left-to-right longest-match decoding, the fast and widely used inference rule underlying WordPiece. We introduce Joint Optimization for Greedy Longest-Match Tokenization (JOLT), which formulates vocabulary learning as an integ
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Article URL: https://fermisense.com/when-machines-take-the-wheel/ Comments URL: https://news.ycombinator.com/item?id=49078454 Points: 317 # Comments: 122
Chinese AI startup Moonshot AI, creators of the increasingly famous "Kimi K" family of powerful, open AI models, today released the full weights for its largest and most performant version yet — Kimi K3 . But enterprises evaluating the model should read the attached custom Kimi K3 usage license as carefully as the benchmark charts. VentureBeat previously covered Kimi K3 when it debuted through Moonshot's hosted API earlier this month, including its 2.8 trillion-parameter architecture, one millio
In the wake of OpenAI’s cyberattack against Hugging Face, few seem ready to acknowledge the implications
Beijing happy to ‘take all necessary measures’ if sanctioned
Anthropic founder and CEO Dario Amodei made his views clear about open-weight models and China's growing AI capabilities.
Siri Expressive Voices synthesize rich, configurable speech in real time and entirely on device, powered by AFM 3 Core Advanced, Apple’s most powerful on-device foundation model. This work presents the memory-efficient audio synthesis architecture behind that capability: a detokenizer that converts the semantic audio tokens emitted by the foundation model into high-fidelity audio within the tight compute and memory budget of the Apple Matrix Coprocessor (AMX). We convert semantic audio tokens to
<p><strong><a href="https://huggingface.co/moonshotai/Kimi-K3">moonshotai/Kimi-K3</a></strong></p> As promised <a href="https://simonwillison.net/2026/Jul/16/kimi-k3/">earlier this month</a>, Moonshot have released the weights for their excellent 2.8 trillion parameter Kimi K3. They're a hefty 1.56TB on Hugging Face.</p> <p>Kimi introduced their own janky <a href="https://huggingface.co/moonshotai/Kimi-K2-Instruct/blob/main/LICENSE">modified version of the MIT license</a> with K2 back in July 20
Article URL: https://www.ft.com/content/ac136522-ecc7-4262-8702-e0d636ea3099 Comments URL: https://news.ycombinator.com/item?id=49077044 Points: 1 # Comments: 0
Just a fun app I've been working on. Not necessarily AI only and works with any AI harness or just as an API. Comments URL: https://news.ycombinator.com/item?id=49076793 Points: 2 # Comments: 0
Article URL: https://telnyx.com/release-notes/kimi-k3-telnyx-inference Comments URL: https://news.ycombinator.com/item?id=49076505 Points: 12 # Comments: 5
Article URL: https://www.semiodept.com/p/ai-labs-dont-know-how-to-make-you Comments URL: https://news.ycombinator.com/item?id=49076433 Points: 2 # Comments: 0
Hope this sparks some conversation. Interested to get the community's take on an app we use internally to find files and provide targeted file content to chatbots. Biggest gains in search are for windows users but I am interested to get feedback from the spotlight users. With the AI export we originally used and MCP connector but when back to drag and drop export of file content due to security and universal applicability. No worries about your file security. Everything runs on-device and works
Article URL: https://github.com/humanlayer/advanced-context-engineering-for-coding-agents/blob/main/benchmarking-opus-5-on-slop-code-bench.md Comments URL: https://news.ycombinator.com/item?id=49076391 Points: 114 # Comments: 28
Article URL: https://github.com/Aidress-ai/Aidress Comments URL: https://news.ycombinator.com/item?id=49076360 Points: 3 # Comments: 0
Article URL: https://www.theregister.com/ai-and-ml/2026/07/27/leading-ai-models-even-grok-are-all-a-bunch-of-leftist-punks/5279208 Comments URL: https://news.ycombinator.com/item?id=49076195 Points: 3 # Comments: 3
Article URL: https://mealyu.com Comments URL: https://news.ycombinator.com/item?id=49076179 Points: 1 # Comments: 0
