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This study focuses on Text-to-Sounding-Video (T2SV) generation, which aims to generate a video with synchronized audio from text, with both modalities aligned to the text conditions. Despite progress in joint audio-video training, two critical challenges remain: (1) text conditioning is a bottleneck—shared captions (TV=TA) trigger modal interference, while a gap persists between dense training captions and concise inference user prompts, and (2) the optimal fusion mechanism for cross-modal featu

Multi-domain fine-tuning of large language models requires improving performance on target domains while preserving performance on constrained domains, such as general knowledge, instruction following, or safety evaluations. Existing data mixing strategies rely on fixed heuristics or adaptive rules that cannot explicitly enforce preservation of such capabilities. We propose DynaMiCS, a dynamic mixture optimizer that casts multi-domain fine-tuning as a constrained optimization problem. At each up

The web is complex, open-ended, and constantly changing, making it challenging to scale training data for visual web agents. Existing data collection attempts remain limited to offline trajectories for supervised fine-tuning or a handful of simulated environments for RL training, thus failing to capture web diversity. We propose Weblica (Web Replica), a framework for constructing reproducible and scalable web environments. Our framework leverages 1) HTTP-level caching to capture and replay stabl

While large language models (LLMs) and coding agents are often applied to user interface (UI) development, developers find it difficult to reliably assess their proficiency in visual and interaction design. Existing evaluations either rely on human experts, who can accurately assess usability by testing critical flows but are slow and costly, or on automated judges, which are scalable but less accurate and opaque. We present FlowEval, a reference-based framework that measures whether a generated

Safety alignment in language models operates through two mechanistically distinct systems: refusal neurons that gate whether harmful knowledge is expressed, and concept neurons that encode the harmful knowledge itself. By targeting a single neuron in each system, we demonstrate both directions of failure — bypassing safety on explicit harmful requests via suppression, and inducing harmful content from innocent prompts via amplification — across seven models spanning two families and 1.7B to 70B

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. OpenAI CEO Sam Altman’s oft-discussed promise that Americans will share in the wealth AI creates was in the news again last week. On Thursday, the Financial Times reported that Altman is in…

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1up4cjh/lingbotvision_masked_boundary_modeling_for/"> <img src="https://preview.redd.it/ha08vg49bnbh1.png?width=140&height=78&auto=webp&s=cbd1e4aed6c0571b7f0acee245c21543fe356719" alt="LingBot-Vision: masked boundary modeling for self-supervised pretraining (0.296 NYUv2 linear-probe RMSE at 1.1B vs 0.309 for DINOv3-7B, trails on ImageNet); weights in 4 sizes[R]" title="LingBot-Vision: masked boundary modeling for self-su

<!-- SC_OFF --><div class="md"><blockquote> </blockquote> <p><strong>Body:</strong></p> <blockquote> </blockquote> </div><!-- SC_ON --> submitted by <a href="https://www.reddit.com/user/Unlikely_Let_9147"> /u/Unlikely_Let_9147 </a> <br/> <span><a href="https://www.reddit.com/r/MachineLearning/comments/1up3kby/edge_ai_asl_recognition_on_raspberry_pi_5_looking/">[link]</a></span> <span><a href="https://www.reddit.com/r/MachineLearning/comments/1up3kby/edge_ai_asl_recognition_on_raspberry_pi_5_look

One morning in 2019, Adebayo Alonge was in a Cape Town hotel room, preparing to demonstrate his startup’s AI answer to a serious problem in African health care: Counterfeit medication, which kills thousands of people across the continent every year. The Rxscanner is a handheld spectrometer that scans a pill with infrared light, then sends the item’s molecular profile to an AI model equipped with a pharmaceutical database. In seconds, the AI identifies the medication from its molecular profile—or

Every year, the International Conference on Machine Learning (ICML) reveals where thousands of AI researchers have decided to put their work. This year’s accepted papers reveal a clear direction: open frontier models and open AI infrastructure have become foundational to how modern AI science gets done. NVIDIA had 74 papers accepted at ICML 2026. Approximately […]

Nations have long invested in domestic infrastructure to advance their economies, protect and use their data, and take advantage of technology opportunities in areas such as transportation, communications, commerce, entertainment and healthcare. AI, the most important technology of our time, is turbocharging innovation across every facet of society. Countries are investing in AI capabilities so […]

<!-- SC_OFF --><div class="md"><p>Built a memory system called TRACE that organizes agent conversation history into a topic tree (branches + summaries) instead of flat RAG chunks, and benchmarked it on MemoryAgentBench (ICLR 2026), specifically the EventQA accurate-retrieval task.</p> <p>Its a pypi package:</p> <p>pip install trace-memory</p> <p>Results (F1):<br/> • TRACE (gpt-oss-20B): 82.5%<br/> • TRACE (gpt-oss-120B): 83.8%<br/> • Mem0 (GPT-4o-mini, paper’s official number): 37.5%<br/> • MemG

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. South Korea’s hottest new bachelors are chip workers Baek, a 35-year-old manager at the South Korean semiconductor titan SK Hynix, was enrolled in a matchmaking company a year ago. In a…

Baek, a 35-year-old manager at the South Korean semiconductor titan SK Hynix, was enrolled in Sunoo, a matchmaking company based in Seoul, a year ago. In a move typical of anxious South Korean parents, his mother signed him up, hoping to find a good wife for her son. Lately, says Baek (who asked to be…

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1uopj0k/emnlp_all_of_the_papers_in_my_review_pool_being/"> <img src="https://preview.redd.it/elmu1a6d0kbh1.png?width=140&height=140&crop=1:1,smart&auto=webp&s=c44181dd02b9668433e47a57a648d98af652fbde" alt="EMNLP: All of the papers in my review pool being detected as AI [D]" title="EMNLP: All of the papers in my review pool being detected as AI [D]" /> </a> </td><td> <!-- SC_OFF --><div class="md"><p><a href="https://prev

Speech-only spoken language models (SLMs) lag behind text and text-speech models in performance, with recent discrete autoregressive (AR) SLMs indicating significant computational and data demands to match text models. Since discretizing continuous speech for AR creates bottlenecks, we explore whether continuous diffusion (CD) SLM is more viable. To quantify the SLMs linguistic quality, we introduce the phoneme Jensen-Shannon divergence (pJSD) metric. Our analysis reveals CD SLMs, mirroring AR b

Language models play a central role in automatic speech recognition (ASR), yet most methods rely on text-only models unaware of ASR error patterns. Recently, large language models (LLMs) have been applied to ASR correction, but introduce latency and hallucination concerns. We revisit ASR error correction with compact seq2seq models, trained on ASR errors from real and synthetic audio. To scale training, we construct synthetic corpora via cascaded TTS and ASR, finding that matching the diversity

Safety policies define what constitutes safe and unsafe AI outputs, guiding data annotation and model development. However, annotation disagreement is pervasive and can stem from multiple sources such as operational failures (annotators misunderstand or misexecute the task), policy ambiguity (policy wording leaves room for interpretation), or value pluralism (different annotators hold different perspectives on safety). Distinguishing these sources matters. For example, operational failures call

<!-- SC_OFF --><div class="md"><p>Hi everyone, I'm currently working on a framework to evaluate the security of LLM applications and AI agents, and I've been stuck on one part for a while.</p> <p>Most red-teaming frameworks rely on an LLM to generate adversarial prompts. My question is more about <strong>which model to use</strong>.</p> <ul> <li>Which <strong>closed-source</strong> models would you recommend for generating high-quality attacks?</li> <li>Which <strong>open-source</strong> models

<!-- SC_OFF --><div class="md"><p>I'm an 18-year-old independent student from Tunisia. I built and I'm leading an open, from-scratch machine-translation pipeline and parallel corpus for Tunisian Darija. Sharing it for feedback.</p> <p>Why: Tunisian Darija, written in Arabizi (Latin letters + numerals like 3/7/9/5 for Arabic phonemes), has almost no open NLP resources. Existing Arabic tools route it through MSA and mishandle the orthography. To the best of my knowledge there was no open parallel

<!-- SC_OFF --><div class="md"><p>I started a PhD in CS about a year an a half ago. Generally speaking my topic is on intrinsic motivation (more commonly people refer to it as unsupervised RL).</p> <p>Intrinsic motivation (IM) is a niche field within AI. It seeks to develop reward signals which are not specific to any task but rather something closer to the low level motivators that drive intelligent behaviors in animals. Some prominent examples are:</p> <ul> <li>Empowerment: <a href="https://ar

<!-- SC_OFF --><div class="md"><p>I am a scientist who just applied machine learning to my research (JEPA/Representation/Geometric branch) and it did wonder! Allowed me to see so many papers that I am still struggling to write up.</p> <p>From what I see, there are clearly a million possibilities not done yet, e.g., industrial data, patterns in nature, etc.</p> <p>Why is the job perspective so pessimistic? We clearly have problems unsolved, and for many, the potential of ML will be proven for sur

<!-- SC_OFF --><div class="md"><p>I made a 10MB LoRA adapter for Qwen3.5-4B plus a small orchestration layer. It decides, per query, whether to answer directly, search the web, or retrieve from your own local documents and it refuses to make things up when it can't verify an answer.</p> <p>It runs locally (Apple Silicon / MLX, with a GGUF build for llama.cpp/Ollama).</p> <p>Basically small instruct models are poor at telling users how confident they really are. They can't verbalise it and tend t

<!-- SC_OFF --><div class="md"><p>As someone who is <strong>not affiliated</strong> with any of the big tech companies, I find it particularly difficult to have the confidence or enthusiasm to approach any ML problem with an attitude that my professors probably had at my stage in life. I'm sure I am not the only one having the following thoughts:</p> <ul> <li>"My research is currently being done better at companies."</li> <li>"ML problem I set out to solve is already solved and in fact turned in

<!-- SC_OFF --><div class="md"><p>I've been trying to come up with a solution for keeping extremely long ai sessions coherent. Sometimes there is too much substance to risk compaction. With so much buzz around diffusion going on it got me thinking, what if we treat the context like a progressive render, blurry>sharp.</p> <p>The practical way to make text "blurry" is compression. This is a "diffusion inspired" system which borrows the coarse-to-fine process, not the formal math. It uses semantic

<!-- SC_OFF --><div class="md"><p>Hi everyone,</p> <p>I built H64LM, a research project to better understand modern LLMs by implementing one from scratch in PyTorch.</p> <p>Instead of relying on high-level training frameworks, I implemented the core components myself attention, MoE routing, normalization, and the training loop.</p> <p><strong>Features</strong></p> <ul> <li>249M-parameter Transformer</li> <li>Grouped Query Attention (GQA)</li> <li>Sparse Mixture-of-Experts (8 experts, Top-2 routi

<!-- SC_OFF --><div class="md"><p>Thinking about getting two months at his website and getting a stronger understanding of machine learning since I am building tools with ai models from hugging face. Have anyone tried it?</p> </div><!-- SC_ON --> submitted by <a href="https://www.reddit.com/user/TheSmashingChamp"> /u/TheSmashingChamp </a> <br/> <span><a href="https://www.reddit.com/r/MachineLearning/comments/1umqf58/tom_yehs_ai_by_hand_is_it_worth_it_d/">[link]</a></span> <span><a href="https://

<!-- SC_OFF --><div class="md"><p>Hi, I am supposed to prepare for SLM and its software part for an on campus internship, i've worked with local models like ollama generally,in my projects and also with open claw so can anyone guide me the last 2-3 days tips on what should i go through for this internship prep??</p> </div><!-- SC_ON --> submitted by <a href="https://www.reddit.com/user/Idea_less_"> /u/Idea_less_ </a> <br/> <span><a href="https://www.reddit.com/r/MachineLearning/comments/1umissr/

The rapid expansion of artificial intelligence infrastructure is typically framed as an energy problem. Data centers are projected to consume a growing share of global electricity demand: The International Energy Agency estimates they could account for 3 to 4 percent of total global consumption within this decade. Utilities are already adjusting long-term forecasts to accommodate anticipated growth from hyperscale facilities and high-density compute clusters. This framing captures scale. It miss

<!-- SC_OFF --><div class="md"><p>​</p> <p>For open-weight LLMs, how practical is it to study defenses against post-release fine-tuning that weakens refusal or safety behavior?</p> <p>I've been seeing “uncensored” or “heretic” variants of new models appear very quickly after release, which raises a question I’m curious about: is fine-tuning resistance a meaningful safety goal for open-weight releases, or is it too narrow because determined users can always modify weights, switch models, or use o

As the parent of two little girls, I often think about how their childhood is different from mine. The seven-year-old is learning about AI at school. The five-year-old is given internet-based homework every week. And they are both absolutely repulsed by the idea of smoking. That was not the prevailing sentiment when I was young.…

arXiv:2607.01235v1 Announce Type: new Abstract: Understanding how Large Language Models (LLMs) make token-level decisions during code generation remains a major challenge for both researchers and practitioners. While recent tools provide insights into model internals or generation outcomes, they often lack decoding-time signals, fine-grained uncertainty measures, and interactive mechanisms for exploring alternative generation paths. We present TokenScope, an interactive interpretability and anal

arXiv:2607.01236v1 Announce Type: new Abstract: As LLM agents gain increasing access to powerful tools, ensuring that their actions are aligned with the user's intent becomes critical. When an agent's proposed tool invocation deviates from the user's intent -- a phenomenon called misalignment -- it may lead to harmful consequences that are difficult to undo. Existing runtime guardrails rely on an LLM-as-a-judge paradigm that lacks a systematic framework for reasoning about alignment, often produ

arXiv:2607.01237v1 Announce Type: new Abstract: Reasoning language models often generate long chain-of-thought (CoT), which accumulates a massive KV cache during the decoding phase and incurs high decoding latency and limited throughput. To address these issues, KV cache compression has emerged as a promising technique for reducing memory overhead by selectively removing unimportant KV pairs while preserving useful ones for subsequent decoding. Nevertheless, we identify two key limitations in ex

arXiv:2607.01239v1 Announce Type: new Abstract: Character-level perturbations bypass safety alignment in modern LLMs despite leaving prompts human-readable. We identify and test a central structural mechanism: BPE tokenization fragments safety-critical words into sub-word pieces, and the three public alignment datasets we surveyed contain no intentionally fragmented inputs. The mechanism is a chain, tested end-to-end on five model families (Qwen-3-4B, Qwen-2.5-7B, Gemma-3-4B, Llama-3.1-8B, Mistr

arXiv:2607.01240v1 Announce Type: new Abstract: Count-based F1 is widely used as a proxy for LLM error-detection quality, but this paper shows that it can rise dramatically without a corresponding improvement in span localization, a gap termed F1 Inflation. The paper introduces ErrorBench, a controlled stress-test protocol for prompt-induced count distortion. ErrorBench evaluates six contemporary LLMs under five prompt conditions over 4,290 responses from 143 CoNLL-2014 passages. Under CoNLL-201

arXiv:2607.01241v1 Announce Type: new Abstract: Existing prompt compression methods treat text as flat token sequences, failing to capture the distributed nature of important information, which is often spread across multiple locations and connected through both local syntactic dependencies and global semantic relations. Such relational structure is naturally represented as a graph, where tokens or sentences become nodes and their dependencies become edges. To this end, we propose RAGP, which fo

arXiv:2607.01245v1 Announce Type: new Abstract: We introduce Office Comprehension Bench (OCB), the first public benchmark to jointly evaluate LLM systems on Word, Excel, and PowerPoint comprehension over native file formats (.docx, .xlsx, .pptx) and their variants. OCB consists of two tracks. File Fidelity Q&A tests structural and visual perception of office artifacts - tables, charts, embedded images, formulas, and app-specific elements such as headers, speaker notes, and named ranges. Domain Q

arXiv:2607.01293v1 Announce Type: new Abstract: We present RuleChef, a framework that uses large language models (LLMs) to generate executable rules for NLP tasks such as text classification, Named Entity Recognition (NER), or relation extraction. Rules are generated based on a task description and a set of labeled examples, then they are iteratively improved based both on additional examples and on human feedback overexisting rules. RuleChef can also be used to bootstrap rules using the observe

arXiv:2607.01392v1 Announce Type: new Abstract: Aligning large language models with diverse and heterogeneous human values requires multi-objective alignment methods to effectively trade off conflicting preference dimensions. Current methods achieve this trade-off by training policies conditioned on preference vectors and leveraging online direct preference optimization. However, exploration uncertainty can cause the reward distributions of responses generated under different preference vectors