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arXiv:2607.10126v1 Announce Type: cross Abstract: Containerization has become increasingly essential in the machine learning (ML) domain, providing reproducibility, portability, and environment consistency. While prior studies have analyzed Dockerfile structures and best practices, none have examined ML projects in depth to reveal how the iterative nature of ML workflows influences container footprint, build performance, and caching behavior. We present the first large scale empirical study of 1

arXiv:2607.10140v1 Announce Type: cross Abstract: Existing optical flow methods broadly follow two paradigms: iterative optimization and diffusion-based estimation. Iterative methods, exemplified by RAFT, achieve high accuracy through recurrent refinement, but remain challenged by large displacements and complex motion. Diffusion-based methods introduce generative modeling and show promise in such ambiguous regions. However, existing diffusion models usually denoise the entire dense flow field f

arXiv:2607.10165v1 Announce Type: cross Abstract: Emotion-aware artistic image generation requires an image to match the input prompt, follow the specified artistic style, and convey the target emotion. In this challenge, the main difficulty is that the visual and affective attributes available in the training data are not explicitly provided at test time. Without these attributes, the generator has to decide not only what to depict, but also how the target emotion should be expressed through co

arXiv:2607.10183v2 Announce Type: cross Abstract: Running large language models on consumer devices such as laptops and desktops is challenging because model weights often exceed GPU memory capacity, making offloading inference necessary to extend effective model capacity with CPU memory. Existing offloading systems, however, typically rely on coarse layer-level or expert-level scheduling, which overlooks substantial heterogeneity among tensors within the same layer and adapts poorly to changing

arXiv:2607.10191v1 Announce Type: cross Abstract: Generative streaming models for Target Speaker Extraction (TSE) commonly exhibit a quality--intelligibility trade-off, wherein naive optimization for perceptual audio quality tends to degrade speech intelligibility, and conversely. We reveal that this trade-off arises not from the constraints of streaming architectures, but from an inappropriate choice of optimization anchor. Directly optimizing against audio quality metrics induces catastrophic

arXiv:2607.10231v1 Announce Type: cross Abstract: Tree crowns are a challenging target for resilient AI because they are not static objects: their spectral response, internal texture, translucency, and apparent boundaries change substantially across the growing season. We develop PhenoEmbed, a self-supervised crown-centric temporal embedding model trained with contrastive and masked reconstruction objectives on HeideBench, an 18-date UAV multispectral time-series benchmark for forest crown pheno

arXiv:2607.10291v1 Announce Type: cross Abstract: Software evolves continuously, yet ensuring that a patch preserves intended behavior without re-verifying an entire codebase remains difficult. Regression verification addresses this problem, but existing techniques require expensive whole-program reasoning or rely on manually written specifications that are rarely available in practice. We present the first contract-based regression verification tool. Contract soundness is ensured by proving all

arXiv:2607.10295v1 Announce Type: cross Abstract: We demonstrate that AI-driven program synthesis can autonomously discover fundamental strategies for decomposing unitary matrices in photonic networks. By extending DreamCoder to complex-valued linear algebra, the system generates decomposition programs achieving the minimal $N(N-1)/2$ Mach-Zehnder interferometers, distinct from both Reck and Clements architectures. Learned programs encode dimension-agnostic invariants: strategies discovered for

arXiv:2607.10341v1 Announce Type: cross Abstract: AI visibility measurement is comparative: practitioners want to know which domains generative search engines cite most often and whether observed differences are large enough to support decisions. Yet the industry lacks a principled way to determine whether enough data has been collected. Collection budgets vary widely across studies and platforms, and conclusions are often drawn from rankings whose stability and precision are unknown. We introdu

arXiv:2607.10358v1 Announce Type: cross Abstract: Foundation models are increasingly used as image feature extractors for mammography, but their robustness under external domain shift remains unclear. We benchmark 15 foundation-model backbones across breast density, BI-RADS severity, and cancer status using a unified frozen-backbone linear-probe protocol, training on 3 source datasets and evaluating on 12 task-compatible out-of-distribution (OOD) datasets after label harmonization. Mammography-s

arXiv:2607.10369v1 Announce Type: cross Abstract: Flow-matching policies have emerged as an effective policy parameterization for robot learning. They iteratively generate actions from noise, enabling highly expressive modeling of complex and multimodal action distributions. However, prior works observed that scaling these policies with value-gradient reinforcement learning (RL) often leads to training instability. Existing methods attribute this instability to iterative generation and therefore

arXiv:2607.10383v2 Announce Type: cross Abstract: Visual Language Navigation foundation models aim to unify deep reasoning for grounded spatial decisions with broad versatility for diverse embodied tasks. Current approaches typically achieve this integration via monolithic policies that map observations directly to actions, yet they often suffer from coordinate drift and poor handling of long-tail semantics. Furthermore, these black-box mappings lack interpretability, hindering the simultaneous

arXiv:2607.10388v1 Announce Type: cross Abstract: Artificial intelligence (AI) is transforming electron microscopy by enabling quantitative analysis of increasingly large and complex datasets for nanoparticle characterization. Recent advances in machine learning (ML) and deep learning (DL) have expanded microscopy from a descriptive imaging technique into a data-driven platform for structural interpretation, dynamic analysis, and scientific inference. This review examines AI methodologies for na

arXiv:2607.10402v1 Announce Type: cross Abstract: Large language models (LLMs) have transformed misinformation from a primarily content-centric problem into a broader ecosystem-level security challenge. When misused, LLMs create risks beyond false content generation, enabling attacks on the social contexts, evidence sources, retrieval corpora, and verification workflows that misinformation defense depends on. In this paper, we introduce a role-layer framework to unify these risks and defenses. T

arXiv:2607.10405v1 Announce Type: cross Abstract: Controlling attributes is a critical step toward achieving the final creative outcome, yet current approaches fall short in supporting users in the iterative refinement of generative content. We propose Spatula, a proof-of-concept system that generates on-demand, in-situ attribute control interfaces and interactions for creating motion graphics. Building on a technical probe that automatically analyzes animation context and generates correspondin

<!-- SC_OFF --><div class="md"><p>I’m training a single-class segmentation model for large rectangular artwork placed on the floor and photographed from above.</p> <p>We have around 3,000 accurately masked original images taken by six different photographers. They are not the same height and do not hold the camera in exactly the same way, so the photos naturally vary in:</p> <ul> <li>roll</li> <li>pitch</li> <li>yaw</li> <li>camera distance</li> <li>object coverage in the frame</li> <li>centerin

<!-- SC_OFF --><div class="md"><p>NB: Reposting due to a typo in the title</p> <p>Putting this here instead of the other subs since I figured a question on deep learning theory is out of place there -- I "recently" found (actually, a few months ago but only just got to reading) <a href="https://ma-lab-berkeley.github.io/deep-representation-learning-book/">a monograph</a> claiming to provide a unified theory of deep learning (and possibly SSL) through the lens of information theory, with one of i

Anthropic's Claude Sonnet 5 narrows the gap to Opus 4.8 on agentic coding, at cheaper Sonnet token pricing. The post Anthropic Claude Sonnet 5 vs Sonnet 4.6 vs Opus 4.8: Agentic Coding Benchmarks, API Pricing, and Cost-Performance Tradeoffs Compared appeared first on MarkTechPost .

Proactive agents that anticipate user needs and autonomously execute tasks hold great promise as digital assistants, yet the lack of realistic user simulation frameworks hinders their development. Existing approaches model apps as flat tool-calling APIs, failing to capture the stateful and sequential nature of user interaction in digital environments and making realistic user simulation infeasible. We introduce Proactive Agent Research Environment (Pare), a framework for building and evaluating

Apple Music serves listeners across 150+ storefronts in dozens of languages, with a catalog that grows by hundreds of thousands of new tracks daily. At this scale, search recall on misspelled, transliterated, and cross-lingual queries becomes a dominant driver of session quality, particularly for tail queries that account for the majority of unique queries. We present a multilingual semantic retrieval system built on a 305M-parameter Siamese bi-encoder fine-tuned from GTE-multilingual-base with

MORPHEUS from Skyfall AI is a persistent enterprise simulation platform for continual reinforcement learning. It runs worlds that never reset, using parameterisable regime shifts and a six-metric evaluation protocol. Across the platform, PPO, HER, EWC, and LCM all remain far below the theoretical upper bound. The post Skyfall AI Releases MORPHEUS: A Persistent Enterprise Simulation Benchmark That Makes Continual Reinforcement Learning Necessary Under Structured Non-Stationarity appeared first on

<!-- SC_OFF --><div class="md"><p>Everyone suddenly seems to be an expert in continual learning. Dario Amodei claimed on the Dwarkesh Patel podcast that continual learning will be achieved by 2026, and Demis Hassabis has called it the most important unsolved breakthrough on the path to more general AI. Two of the most prominent people in the field, and yet I don't think there's even a consensus on what continual learning actually means.</p> <p>I see researchers and startups approaching it in fun

<!-- SC_OFF --><div class="md"><p>Disclosure: I built it, it is open source, Apache-2.0 licensed, and currently alpha. Repository: <a href="https://github.com/mireklzicar/gpuhedge">https://github.com/mireklzicar/gpuhedge</a></p> <p>I started working on it after benchmarking a 17 GB AI model across several serverless GPU providers.</p> <p>On the primary provider, requests usually either completed in roughly 6–8 seconds or took around 90–122 seconds after a fresh GPU cold start. Simply switching t

In this tutorial, we reconstruct the VideoAgent workflow as a runnable, API-key-free multi-agent pipeline. We build an intent parser, an agent library, a tool router, a graph planner, and a textual-gradient optimizer that repairs the execution graph. We wire these planning components to FFmpeg, Whisper transcription, scene detection, keyframe sampling, captioning, cross-modal indexing, and beat-synced editing. By the end, we have a system that answers questions about a video, summarizes it, and

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Anthropic—currently the world’s most valuable AI company, with a nearly $1 trillion valuation—has a reputation for publishing strange and heady research. It’s looking into whether AI models can feel pain, for example,…

<!-- SC_OFF --><div class="md"><p>Read a long piece on the future of LLM reasoning that makes a provocative claim: Chain of Thought is a useful hack but we've started to confuse a readable trace with the actual computation. All in all, "generating text is not the same as thinking."</p> <p>There are two practical problems here:</p> <ol> <li><p>Faithfulness: CoT style traces can decouple from what the model actually "did." u can get plausible steps with a wrong answer, or messy steps with a right

<!-- SC_OFF --><div class="md"><p>LLM's seem to make it so much easier to run experiments, write papers, etc. As a result, are we seeing phd students finish their phds sooner than ever before specifically in CS? If not, why not?</p> </div><!-- SC_ON --> submitted by <a href="https://www.reddit.com/user/Alone_Reality3726"> /u/Alone_Reality3726 </a> <br/> <span><a href="https://www.reddit.com/r/MachineLearning/comments/1uvhr7a/fast_track_through_a_cs_phd_using_llms_for_paper/">[link]</a></span> <s

Software engineering jobs are under threat from AI . Some applicants are fighting back by using AI in the interview process, employing AI assistants that suggest responses on the fly during remote technical interviews. Meanwhile, some employers are countering with—you guessed it—AI. They’re applying AI-powered tools to detect telltale signs of AI use during interviews. This two-sided dynamic is turning hiring into an AI arms race with no clear winners. Yet as interviewers and interviewees naviga

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. Sperm donors need limits, says a European fertility group Ties van der Meer doesn’t know how many siblings he has. The 47-year-old was conceived at a private fertility clinic using sperm…

This article is brought to you by X Square Robot . Large language models gave artificial intelligence a working recipe. Pretrain a large model on broad data, and general capability follows. Robotics has no such recipe. Robotics systems have long been assembled from separate perception, planning, and control parts that rarely add up to intelligence a robot can carry from one task to another, or one machine to another. The central problem in embodied AI is to find the equivalent recipe, and the fi

Agentic LLMs keep failing the same way because they lack specific, reusable capabilities. Stanford's TRACE diagnoses those gaps from an agent's own trajectories, synthesizes one verifiable training environment per capability, trains a LoRA adapter for each, and routes tokens across experts—improving τ²-Bench by +15.3 points and reaching 73.2% Pass@1 on SWE-bench Verified. The post Stanford Researchers Introduce TRACE: A Capability-Targeted Agentic Training System That Turns Recurrent Agent Failu

<!-- SC_OFF --><div class="md"><p>Anthropic’s Jacobian Lens work introduced a way to inspect verbalizable representations inside language models. Follow-up experiments suggested that entropy in this internal “workspace” might help identify confidently incorrect answers.</p> <p>I tested that hypothesis on Qwen3-4B across ~11,400 examples from seven distinct datasets, including TriviaQA, PopQA, NQ-Open, TruthfulQA, HotpotQA, GSM8K, and CommonSenseQA.</p> <p>Three main findings:</p> <ol> <li>It can

Prime Intellect launched verifiers 0.2.0, previewing a rewritten "v1" core under the verifiers.v1 namespace. It splits an environment into a taskset (what), a harness (how), and a runtime (where), with an interception server that proxies requests and records training-ready traces. Any taskset runs under any compatible harness, with full prime-rl training support at launch. The post Prime Intellect Releases Verifiers v1: Composable Tasksets, Harnesses, and Runtimes for Agentic RL Training and Eva

<!-- SC_OFF --><div class="md"><p>"<a href="https://arxiv.org/abs/2510.01171">Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity</a>"</p> <p>This paper was accepted to ICML this year. Its main idea is a very simple prompt-engineering trick: "changing the prompt this way led to more diverse sampling". Naturally, it is difficult to provide a rigorous theoretical analysis for something like this.</p> <p>Even if it works, I’m not sure this kind of prompt engineering belongs

arXiv:2607.08773v1 Announce Type: new Abstract: In this work we present a rigorous theoretical framework to a foundational problem of AI safety, namely adversarial robustness. In particular, we show that the adversarial robustness problem can be reduced to a lattice traversal problem. Each element of this lattice corresponds to an interval, i.e., an axis-aligned hyper-rectangle, containing an input point $\mathbf{x}$. Consider a multilayered perceptron classifier (MLP). An interval $I$ constitut

arXiv:2607.08774v1 Announce Type: new Abstract: Reliability in large language model (LLM) systems is typically framed as a function of model capability. We challenge this by demonstrating that reliability is significantly influenced by \emph{inference-time control} -- the computational layer governing task framing and context selection. We introduce \emph{CogniConsole}, an architectural instantiation that externalizes this control into a structured interface combining programmatic coordination w

arXiv:2607.08894v1 Announce Type: new Abstract: Large Language Model (LLM) agents have shown promise in multi-step planning tasks, but existing approaches like LATS (Language Agent Tree Search) and ReAct rely heavily on LLM inference during planning, leading to high computational costs and stochastic behavior. We present \textbf{GATS} (Graph-Augmented Tree Search), a planning framework that combines systematic UCB1-based tree search with a layered world model to eliminate LLM calls during infere

arXiv:2607.08964v1 Announce Type: new Abstract: AI agents have become capable of autonomously completing short, well-specified tasks. However, existing terminal benchmarks largely focus on simple problems that finish within minutes and are evaluated only by their final outcome. This setup overlooks intermediate progress and partial solutions, yielding sparse reward signals and an incomplete picture of agent capability. We introduce Long-Horizon-Terminal-Bench, a terminal benchmark of 46 long-hor

arXiv:2607.08986v1 Announce Type: new Abstract: We formalize a research result in the Lean 4 proof assistant by having a mathematician direct an AI system, and frame the activity as a formalization game. The objective is to turn a LaTeX document into Lean. The game is won when the development compiles, contains no sorry, and a machine check shows the target theorems rest on Lean's foundational axioms alone. Reuse is a second check, by a definition we introduce: whether the development yields a s

arXiv:2607.09059v1 Announce Type: new Abstract: We present ARCANA, a collaborative multi agent framework for solving ARC AGI 2 tasks under strict test time and hardware constraints. ARCANA decomposes each task into iterative perception, hypothesis generation, symbolic execution, and reflective refinement. A perceptual grounding agent builds object centric scene graphs from raw grids, a latent program policy proposes diverse DSL programs, a symbolic executor verifies candidates on demonstrations,