Digest Archive

We are releasing FIKA-Bench: From Fine-grained Recognition to Fine-Grained Knowledge Acquisition , a benchmark for evaluating whether multimodal agents like OpenClaw can actively acquire fine-grained knowledge from external evidence. The motivation is that many fine-grained visual recognition benchmarks are still close to a closed-set classification setting: given an image, the model is expected to output a label...

The proliferation of large language models (LLMs) and modular skills has endowed autonomous agents with increasingly powerful capabilities. Existing frameworks typically rely on monolithic LLMs and fixed logic to interface with these skills. This gives rise to a critical bottleneck: different LLMs offer distinct advantages across diverse domains, yet current frameworks fail to exploit the complementary strengths of...

Public transit route planning traditionally depends on structured map infrastructure and complex routing engines, and no existing dataset supports training models to bypass this dependency. We present TransitLM, a large-scale dataset of over 13 million transit route planning records from four Chinese cities covering 120,845 stations and 13,666 lines, released as a continual pre-training corpus and benchmark data for...

Spreadsheet systems (e.g., Microsoft Excel, Google Sheets) play a central role in modern data-centric workflows. As AI agents grow increasingly capable of automating complex tasks, such as controlling computers and generating presentations, building an AI-driven spreadsheet agent has emerged as a promising research direction. Most existing spreadsheet agents rely on specialized prompting over general-purpose LLMs...

Large language models (LLMs) and agentic systems have shown promise for clinical decision support, but existing works largely assume that evidence has already been curated and handed to the model. Real-world clinical workflows instead require agents to actively seek, iteratively plan, and synthesize multimodal evidence from heterogeneous sources. In this paper, we introduce ClinSeekAgent, an automated agentic...

The rise of personal assistant agents, e.g., OpenClaw, highlights the growing potential of large language models to support users across everyday life and work. A core challenge in these settings is proactive assistance, since users often begin with underspecified requests and leave important needs, constraints, or preferences unstated. However, existing benchmarks rarely evaluate whether agents can identify and act...

Disclosure first: I work on community at MiroMind. One of our researchers just dropped the full MOOSE-Star collection on Hugging Face — a 7B model post-trained for scientific hypothesis discovery, plus the dataset behind it. Paper accepted at ICML 2026. 🤗 Collection: Inside: MS-IR-7B / MS-HC-7B / MS-7B : 7B models for inspiration retrieval, hypothesis composition, and joint use. Base: DeepSeek-R1-Distill-Qwen-7B....

Large language and vision-language models increasingly power agents that act on a user's behalf through command-line interface (CLI) harnesses. However, most agent benchmarks still rely on synthetic sandboxes, short-horizon tasks, mock-service APIs, and final-answer checks, leaving open whether agents can complete realistic long-horizon work in the runtimes where they are deployed. This work presents WildClawBench...

Code: Paper: HF: ; ; Disclosure: co-author. Idea: Inject a trainable diffusion attention module into each layer of a frozen AR Transformer. Both heads share one KV cache. Diffusion head projects K=32 tokens in parallel; AR head verifies in a second pass and accepts the longest matching prefix. Output distribution is provably identical to the base model. Results: Up to 7.8× TPF, ~6× wall-clock on MATH-500. 16% of...

Agentic modeling aims to transform LLMs into autonomous agents capable of solving complex tasks through planning, reasoning, tool use, and multi-turn interaction with environments. Despite major investment, open research remains constrained by infrastructure and training gaps. Many high-performing systems rely on proprietary codebases, models, or services, while most open-source frameworks focus on orchestration and...

We introduce CurveBench, a benchmark for hierarchical topological reasoning from visual input. CurveBench consists of 756 images of pairwise non-intersecting Jordan curves across easy, polygonal, topographic-inspired, maze-like, and dense counting configurations. Each image is annotated with a rooted tree encoding the containment relations between planar regions. We formulate the task as structured prediction: given...

We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling...

I was thinking, that some folks in this community will be interested to see what current options are on local deep research field. So I spent some time to collect everything I could find together. Enjoy. TLDR: the most healthiest and local-friendly projects are "GPT Researcher" by assafelovic and "Local Deep Research" by LearningCircuit. "Local Deep Research" by LearningCircuit Observations: python alive - last...

The emergence of "vibe coding" platforms, where users describe applications in natural language and AI agents autonomously generate full-stack software, has created a need for rigorous evaluation beyond code-level benchmarks. In order to assess them as virtual software development agencies on understanding business requirements, making architectural decisions, writing production code, handling iterative...

We present our winning system for Task~B (generation with reference passages) in SemEval-2026 Task~8: MTRAGEval. Our method is a heterogeneous ensemble of seven LLMs with two prompting variants, where a GPT-4o-mini judge selects the best candidate per instance. We ranked 1st out of 26 teams, achieving a conditioned harmonic mean of 0.7827 and outperforming the strongest baseline (gpt-oss-120b, 0.6390). Ablations...

Robotic systems that interact with the physical world must reason about kinematic and dynamic constraints imposed by their own embodiment, their environment, and the task at hand. We introduce KinDER, a benchmark for Kinematic and Dynamic Embodied Reasoning that targets physical reasoning challenges arising in robot learning and planning. KinDER comprises 25 procedurally generated environments, a...

Recent advances in large language models have led to strong performance on reasoning and environment-interaction tasks, yet their ability for creative problem-solving remains underexplored. We study this capability through the lens of creative tool use, where a model repurposes available objects by reasoning about their affordances and attributes rather than relying on canonical usage. As a first step, we introduce...

Large language models (LLMs) are increasingly used as interactive agents, but optimizing them for long-horizon decision making remains difficult because current methods are largely purely reactive, which weakens both exploration and credit assignment over extended trajectories. In this work, we present Strategic Trajectory Abstraction (StraTA), a simple framework that introduces an explicit trajectory-level strategy...

Agentic large language model systems have demonstrated strong capabilities. However, their reliance on language as the universal interface fundamentally limits their applicability to many real-world problems, especially in scientific domains where domain-specific foundation models have been developed to address specialized tasks beyond natural language. In this work, we introduce Eywa, a heterogeneous agentic...

Large Language Models are being increasingly deployed as the decision-making core of autonomous agents capable of effecting change in external environments. Yet, in conversational benchmarks, which simulate real-world customer-centric issue resolution scenarios, these agents frequently fail due to the cascading effects of incorrect decision-making. These challenges are particularly pronounced for open-source LLMs...

The discovery of novel materials is critical for global energy and quantum technology transitions. While deep learning has fundamentally reshaped this landscape, existing predictive or generative models typically operate in isolation, lacking the autonomous orchestration required to execute the full discovery process. Here we present ElementsClaw, an agentic framework for materials discovery that synergizes Large...

Fine-tuning Large Language Models (LLMs) on consumer-grade GPUs is highly cost-effective, yet constrained by limited GPU memory and slow PCIe interconnects. Pipeline parallelism combined with CPU offloading mitigates these hardware bottlenecks by reducing communication overhead. However, existing PP schedules suffer from an inherent limitation termed the weight binding issue. Binding uneven model stages (e.g., the...

GIANT e-ink display LIVE in my house and actively removing the “mental load” of motherhood 😅 Turns out my household chaos just needed to be tamed by a display my team of @openclaw and @NousResearch Hermes agents manage for me 💅. (GIANT e-ink display LIVE in my house and actively removing the “mental load” of motherhood 😅 Turns out my household ...)

A few weeks ago, ARC-AGI 3 was released. For those unfamiliar, it’s a benchmark designed to study agentic intelligence through interactive environments. I'm a big fan of these kinds of benchmarks as IMO they reveal so much more about the capabilities and limits of agentic AI than static Q&A benchmarks. They are also more intuitive to understand when you are able to actually see how the model behaves in these...

been spending $200+/day on claude code and had zero visibility into what was eating the tokens. ccusage shows cost per model per day which is great but i wanted to know - is it the debugging thats expensive? the brainstorming? which project is burning the most? it reads the session transcripts claude code already stores on disk (\~/.claude/projects/) and classifies every turn into 13 categories based on tool usage...

i'm building agents for procurement & one thread has been to let claude systematically deconstruct a website so agents can navigate them. but as i've been doing this, like a piñata, interesting things keep falling off -- from trackers, to interesting feature flags to even some over-exposed data. so i naturally claude-coded it into an oss repo \+ website (teardown.fyi) i've run it for about \~125 websites so far -...

TL;DR: Claude Code v2.1.100+ silently adds ~20K invisible tokens to every request, server-side. This eats your limits faster AND may degrade output quality. Downgrade to v2.1.98 for immediate relief. Proxy evidence below. --- I run Claude Code Max (5x plan) heavily — 3-5 parallel sessions, custom orchestration, the whole deal. Two weeks ago my usage limits started hitting way earlier than expected. What used to last...

Just type `/extract-design` ` in Claude Code and it pulls the entire design language — colors, fonts, spacing, shadows, components, everything. The main output is a markdown file specifically structured for Claude to understand. So you can extract a site's design, then tell Claude "build me a landing page using this design system" and it actually nails it because it has the exact tokens, scales, and component...

Claude cooked on the code, but I wrote this post myself, caveman style. I wanted to play with Qwen3.5-122B, but I don't have a unified memory system to work with, and 15 tok/s was rough. 23 tok/s is still rough but honestly noticeably faster when streaming responses. Tl;dr: We keep track of which experts get routed to most frequently for the past N tokens. We make a bet that the processing speed-up from loading...

Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software. It’s powered by our newest frontier model, Claude Mythos Preview, which can find software vulnerabilities better than all but the most skilled humans. (Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software. It’s powered b...)

We're bringing the advisor strategy to the Claude Platform. Pair Opus as an advisor with Sonnet or Haiku as an executor, and get near Opus-level intelligence in your agents at a fraction of the cost. (We're bringing the advisor strategy to the Claude Platform. Pair Opus as an advisor with Sonnet or Haiku as an execut...)

We just gave every Hermes Agent a free cloud browser. Use the Browser Use ecosystem in Hermes Agent for free > Unlimited browser hours > Free proxies > Persistent authentication @NousResearch 🤝 Browser Use. (We just gave every Hermes Agent a free cloud browser. Use the Browser Use ecosystem in Hermes Agent for free > Unl...)

Over the past several days, there has been a lot of internet discourse around Claude Mythos being held back from public release. Many people have been claiming this is somehow yet another devious marketing tactic meant to somehow weigh down Dario's pocketbook by... not letting people pay to access the model. Claims of hype and power consolidation and other self-congratulatory motives are easy to find online, but I...

Stella Laurenzo, AMD’s director of AI, filed a detailed GitHub issue on April 2 documenting that Claude Code reads code three times less before editing it, rewrites entire files twice as often, and abandons tasks mid-way at rates that were previously zero. Her analysis of nearly 7,000 sessions puts precise numbers on how Anthropic’s coding tool has degraded since early March. PERFORMANCE DECLINE: AMD’s AI director...

This is vindicating for all the people that have been screaming out that Anthropic simply doesn't want to release Mythos because they do not have the compute, not because the model is "too powerful." Summary of the findings: >On April 2, AMD’s Director of AI, Stella Laurenzo, filed a GitHub issue detailing a severe degradation in Claude Code's performance since early March. Based on an analysis of nearly 7,000...

Agent skills, which are reusable, domain-specific knowledge artifacts, have become a popular mechanism for extending LLM-based agents, yet formally benchmarking skill usage performance remains scarce. Existing skill benchmarking efforts focus on overly idealized conditions, where LLMs are directly provided with hand-crafted, narrowly-tailored task-specific skills for each task, whereas in many realistic settings...

Multimodal LLM agents operating in complex game environments must continually reuse past experience to solve new tasks efficiently. In this work, we propose Echo, a transfer-oriented memory framework that enables agents to derive actionable knowledge from prior interactions rather than treating memory as a passive repository of static records. To make transfer explicit, Echo decomposes reusable knowledge into five...

We propose a new frontier: Neural Computers (NCs) -- an emerging machine form that unifies computation, memory, and I/O in a learned runtime state. Unlike conventional computers, which execute explicit programs, agents, which act over external execution environments, and world models, which learn environment dynamics, NCs aim to make the model itself the running computer. Our long-term goal is the Completely Neural...

Mixture-of-Experts (MoE) large language models (LLMs) are among the top-performing architectures. The largest models, often with hundreds of billions of parameters, pose significant memory challenges for deployment. Traditional approaches to reduce memory requirements include weight pruning and quantization. Motivated by the Router-weighted Expert Activation Pruning (REAP) that prunes experts, we propose a novel...

The viability of chain-of-thought (CoT) monitoring hinges on models being unable to reason effectively in their latent representations. Yet little is known about the limits of such latent reasoning in LLMs. We test these limits by studying whether models can discover multi-step planning strategies without supervision on intermediate steps and execute them latently, within a single forward pass. Using graph...

After the Claude Code source code leak, a former PM extracted its multi-agent orchestration system into an open source model agnostic framework. He studied the architecture, focused on the multi-agent orchestration layer (the coordinator that breaks goals into tasks, team. (After the Claude Code source code leak, a former PM extracted its multi-agent orchestration system into an open sourc...)

Zero-dependency library of production-ready WebGL and Canvas 2D shaders with multiple color themes. Copy, integrate, ship.

Ian Curtis demoed a parallax window effect using Blender → World Labs Gaussian splat → Three.js with MediaPipe face tracking for off-axis projection.

Ollin Boer Bohan trained a neural network to reproduce a forest trail near his apartment as an interactive web experience — a tiny world model you can walk through in the browser.

A single-stream transformer architecture for joint audio-video generation that's significantly faster than multi-stream approaches while maintaining quality. 115 HF upvotes.

A stable end-to-end Joint-Embedding Predictive Architecture that learns world models directly from pixel observations — continuing the LeCun JEPA lineage.

Top-trending HF paper this week (144 upvotes). Technical report on a new speech synthesis approach.

Cohere released an open-source transcription model that runs in-browser with weights on HuggingFace. Claims state-of-the-art for open models.

Major update to the browser automation CLI — 2x faster, half the cost, direct Chrome DevTools Protocol integration instead of high-level abstractions.

Former React team member Cheng Lou dropped what he calls one of the more important foundational pieces of UI engineering for the foreseeable future. 29K likes, massively viral.

Salesforce CEO Benioff posted video of Figure's latest humanoid robot handling packages with notable dexterity improvements.

An Australian tech entrepreneur used ChatGPT and AlphaFold to design a custom mRNA vaccine that significantly reduced his dog's tumor. Researchers involved are reportedly excited by the results.

Bilawal Sidhu deployed an AI agent swarm to capture every OSINT signal during Operation Epic Fury, then built a full 4D temporal reconstruction in WorldView — scrubbing through the strikes minute by minute.

Control your coding agents like units in a real-time strategy game. Early but creative take on agent orchestration UX.

Paper documenting how safety guardrails in frontier models can catastrophically collapse under certain conditions. 30 HF upvotes.

Legislation introduced to pause AI data center building and pursue international coordination, plus a ban on chip exports. The political angle on AI scaling heats up.

DLSS 5 appears to generate AI imagery on top of upscaling rather than just enhance existing frames — characters look like different people, drawing sharp criticism from gamers and CG professionals.

Open-source library of ultra-realistic shader effects for the web — multiple themes, zero dependencies, copy-paste integration.

Demo combining Blender → World Labs detailed splat generation → Three.js rendering with MediaPipe face tracking for parallax window effect.

Technique generating AI video facial animations (LTX 2), extracting depth maps, and projecting onto face meshes via vertex displacement — L.A. Noire-like results for indie games.

Ollin Boer Bohan trained a neural network to mimic a real forest trail near his apartment, with an interactive web demo for real-time exploration.

Replaces fixed residual accumulation with softmax attention over preceding layer outputs — 2.11% downstream gain at negligible overhead. Addresses hidden-state growth diluting layer contributions.

Each attention head attends to both sequence KV pairs at current layer AND depth KV pairs from preceding layers — 2.11% downstream improvement with 97.3% FlashAttention-2 efficiency.

Decouples video editing into semantic anchoring and motion alignment — pre-training on motion-centric tasks alone yields strong zero-shot editing. Competitive with Kling-Omni.

Decouples spatial geometry from temporal motion via 1-frame optimization, enabling genuine 3D-aware video customization for VR/AR and virtual production without multi-view video datasets.

Maintains and updates spatial evidence from unbounded video streams using test-time training with fast weights — hybrid architecture with sliding-window attention and 3D spatiotemporal convolution.

NVIDIA's 30B MoE with only 3B activated params matches frontier models on IMO/IOI/ICPC — 20× fewer parameters than DeepSeek equivalent. Open weights released.

First framework to distill bidirectional audio-visual diffusion into a streaming autoregressive generator — solves extreme token sparsity and cross-modal sync issues for real-time generation.

Handles geometric reconstruction from long video sequences using hybrid memory architecture.

Major update to browser automation tool — direct Chrome DevTools Protocol, half the cost, connect to running Chrome instances.

Three-stage motion generation framework using a novel diffusion-based discrete tokenizer — reduces trajectory error from 0.72cm to 0.08cm while using 1/6 the tokens.

Exploits the insight that sparse attention indices are highly redundant across adjacent layers — shares indices via 'Full' and 'Shared' layers with multi-layer distillation, significantly accelerating prefill and decode.

Lightweight adapter injects 3D skeleton joint coordinates and topology into frozen 3D generation backbones via cross-attention — enables precise structural articulation control and local 3D editing.

Performs diffusion directly within coherent 3D representation space instead of 2D image/video latents — uses 3D-URAE autoencoder with cross-view correspondence loss for consistent scene generation.

Meta's V-JEPA 2.1 achieves SOTA on egocentric anticipation, robotic grasping (+20pt over V-JEPA-2), depth estimation, and navigation — dense predictive loss with deep self-supervision across encoder layers.

SuperSplat ships first-person walk mode with WASD controls, streamed LOD, and easy upload — making Gaussian Splat scenes navigable and shareable like Google Street View.

First framework to deterministically convert pre-trained video diffusion models into single-pass depth regressors — eliminates stochastic sampling and scale drift.

Replaces static conditioning concatenation in ControlNet with a latent-attention router that dynamically selects expert pathways per diffusion timestep — reduces artifacts from conflicting modalities.

Uses executable code as the reasoning step before image generation — produces structured drafts via code then renders, excelling at complex spatial layouts and embedded text.

Drop-in DiT mechanism that inserts a learnable variable-length latent interface, enabling runtime quality-speed tradeoffs without retraining.

Parameter-free linear compensation for dropped attention blocks in video generation via error-aware routing — ~2x speedup with minimal quality loss.

Compresses visual observations to just 8 discrete tokens for action-conditioned world models, making real-time planning computationally feasible.

Exploits cross-layer redundancy in sparse attention indexers — reuses top-k token selections across layers, cutting prefill latency 2-3x with negligible quality loss.

Tackles reward model hallucinations in RL-guided image editing with curated data, base-and-bonus reward strategy, and new FIRM-Bench benchmark.

Michael Livs extracted Anthropic's generative UI design system from conversation exports, rebuilt it with live-streaming HTML via morphdom DOM diffing into native macOS windows.

Open-source tool that translates UE Blueprint visual scripts to C++ — useful for game dev teams migrating visual prototypes to production code.

Nvidia's new open model punches significantly above its weight class — r/LocalLLaMA calls it a bigger deal than the marketing suggests, with 152 comments of analysis.

Combines spatial and frequency-domain analysis for deepfake detection with manipulation heatmaps — open-sourced university project on r/MachineLearning.

Han Xiao built pure MLX implementations of UMAP/t-SNE/PaCMAP with a scatter-add alpha-blending splatting renderer on Metal — 70K points from raw data to rendered video in seconds.

Trained RL agents on 1 petabyte / 12,000 years of data on a single server — algorithmic breakthroughs, massively faster training, 10 new environments.

Scales dense 3D reconstruction to extremely long video sequences using hybrid memory — bidirectional priors within chunks, sliding window attention across chunks. No post-optimization needed.

Encodes observations into just 8 discrete tokens for latent world models, making real-time planning computationally feasible. Conventional tokenizers use hundreds of tokens per frame.

Systematic comparison shows distilled small Qwen3 models outperform GPT-5, Claude Opus, and Gemini Flash on specific classification and function-calling tasks. The small model advantage is real for focused use cases.

Combines spatial and frequency-domain analysis to detect deepfakes and show exactly where the face was manipulated. Open-source, with visual heatmap output.

Microsoft launched Copilot Cowork — an agent that executes multi-step workflows across Outlook, Teams, Excel, and PowerPoint autonomously. Notably built on Anthropic's Claude, not OpenAI.

Actor-18M dataset + asymmetric identity-preserving attention for consistent full-body identity across dynamic shots and viewpoints. Addresses the copy-paste artifact problem in human video gen.

Ido Salomon released AgentCraft v1 — an RTS-style interface for controlling coding agents. Early but fun concept: `npx @idosal/agentcraft`.

Multiple impressive LTX 2.3 text-to-video generations on r/StableDiffusion showing significant quality jumps in open-source video models.

CS student built a Blender add-on using real-time webcam head tracking to create natural depth perception while navigating 3D scenes. Free and open-source.

Open-source tool that translates Unreal Engine Blueprints to C++ code. Useful for performance-critical game dev workflows.

Early access users report SD4 moves to a DiT-based architecture with native video support. Quality appears to match DALL-E 3 in initial comparisons.

OpenAI's Codex CLI now supports loading entire project directories into context, with smart chunking that preserves cross-file references.

New paper from Meta demonstrates instant mesh extraction from trained NeRFs, making the NeRF→production pipeline viable for real-time applications.

Multiple sources suggest GPT-5 will unify text, image, video, and audio generation in a single model. No official confirmation from OpenAI.

New standardized benchmark for neural rendering methods covers speed, quality, and memory across 50 scenes. 3DGS variants dominate speed; NeRF variants win on quality.

Cursor 0.45 introduces automatic merge conflict resolution that understands both sides' intent. Early reports say it handles 80%+ of conflicts correctly.

Paper shows a training technique that gives diffusion models accurate object counting, solving one of the oldest failure modes in image generation.