spoken dialogue modelco-speech motionSource HF Daily Papers
Motion-Omni: End-to-End Joint Speech and Full-Body Motion for Spoken Dialogue38 upvotes2 comments
spoken dialogue model4 authorsno limits stated
What we saw
“Motion-Omni is an end-to-end framework that jointly generates spoken dialogue and full-body co-speech motion from shared hidden…”
They claim — We further release SwDA-500 and, to our knowledge, the first public evaluation protocol for stochastic open-ended full-body spoken dialogue, matching audio across motion systems while uni…
Published 2026-08-27
Fetched 2026-09-07 23:01 UTC · 1ae2e3cb2c0f6040
Torn from — the abstract
Thread ↗ agentic coordination scalingenvironment scalingSource HF Daily Papers
Apodex 1.1: Scaling Agentic Intelligence for Complex Work147 upvotes1 comments
agentic coordination scaling71 authorsno limits stated
What we saw
“Apodex 1.1 improves sustained, verifiable progress on complex real-world tasks by scaling executable environments and training …”
They claim — Across complex professional work, finance, scientific research, mathematics, coding, and search, Apodex 1.1 reaches the leading performance band despite using a substantially smaller mode…
Published 2026-08-23
Fetched 2026-08-25 10:14 UTC · 9a7393307ddcffe8
Torn from — the abstract
Thread ↗ tool-callingmobile planning agentsSource HF Daily Papers
omnimodal world modelenterable generative mediaSource HF Daily Papers
EchoWM: Open and Enterable Omnimodal World Models50 upvotes1 comments
omnimodal world model22 authorsno limits stated
What we saw
“EchoWM is an omnimodal world model that generates synchronized high-resolution video, sound, music, and speech while following …”
Published 2026-08-23
Fetched 2026-08-25 10:14 UTC · 13b94b4d054c5e64
Torn from — the abstract
Thread ↗ infinite video editingstreaming video generatorSource HF Daily Papers
Prompt EngineeringContext EngineeringSource HF Daily Papers
omni-modal large language modelsOmniAssistBenchSource HF Daily Papers
OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs28 upvotes2 comments
omni-modal large language models9 authorsstates limits
What we saw
“OmniAssistBench evaluates real-time interactive video assistants by reverse-engineering multi-turn interaction videos, revealin…”
They claim — Results show that the proprietary Gemini-3-Pro reaches 66.4 out of the max point of 100, while the open-source Qwen3-Omni-Instruct achieves 51.2.
Published 2026-08-20
Fetched 2026-08-25 10:14 UTC · cb7314dbba846623
Torn from — the abstract
Thread ↗ omni-modal understandingPer-vGridSource HF Daily Papers
Mixture-of-ExpertsMaximal Update ParameterizationSource HF Daily Papers
SWE-bench Sciencescientific software engineeringSource HF Daily Papers
SWE-bench Science: Can Coding Agents Resolve Engineering Tasks in Science?61 upvotes4 comments
SWE-bench Science5 authorsstates limits
What we saw
“SWE-bench Science benchmarks coding agents on scientific software repair, revealing failure mechanisms and mixed effects of sci…”
They claim — Even the best-performing agent, Claude Code with Opus-5 (max), achieves a pass@1 below 50%, highlighting the substantial challenges posed by scientific software engineering.
Published 2026-08-19
Fetched 2026-08-25 10:14 UTC · bbf6eac293bcc02d
Torn from — the abstract
Thread ↗ parallel reasoningtemporal confidenceSource HF Daily Papers
ParaTempo: Efficient Parallel Reasoning via Temporal Confidence29 upvotes3 comments
parallel reasoning8 authorsstates limits
What we saw
“ParaTempo improves parallel reasoning efficiency by using temporal confidence to dynamically prune, retire, and reallocate reas…”
They claim — Experiments on challenging mathematical and scientific reasoning benchmarks show that ParaTempo reduces average latency by 21.8-32.2% and total token usage by 18.1-30.3% while maintaining…
Published 2026-08-17
Fetched 2026-08-25 10:14 UTC · 0f7a16969a7c76aa
Torn from — the abstract
Thread ↗ multimodal large language modelshybrid-thinkingSource HF Daily Papers
text-to-image diffusion modelsedit concept granularitySource HF Daily Papers
Unlocking the Potential of Image Editing via Concept Scaling and Dense Supervision42 upvotes1 comments
text-to-image diffusion models7 authorsno limits stated
What we saw
“A hierarchical taxonomy and dense supervision strategy improve diffusion-based image editing through fine-grained concepts, lar…”
They claim — To address these issues, we establish a comprehensive hierarchical taxonomy featuring over 1,000 fine-grained edit concepts and build ConceptEdit-12M, a massive dataset of 12 million high…
Published 2026-08-16
Fetched 2026-08-25 10:14 UTC · 38d02ea00a52b522
Torn from — the abstract
Thread ↗ reinforcement learningself-rewarding RLSource HF Daily Papers
programmable logic controllers (PLCs)program organization units (POUs)Source HF Daily Papers
evolution strategiesreinforcement learningSource HF Daily Papers
Source HF Daily Papers
Large Vision-Language ModelsPixel-to-3D Action FormulationSource HF Daily Papers
video generationvision-language modelSource HF Daily Papers
MoE servingexpert residencySource HF Daily Papers
closed-loop embodied harnesscode-based runtime criticsSource HF Daily Papers
vision-language modelgated cross-attentionSource HF Daily Papers
MOSS-VL Technical Report42 upvotes2 comments
vision-language model32 authorsno limits stated
What we saw
“MOSS-VL is an open vision-language model family e…”
Published 2026-08-14
Fetched 2026-08-20 07:11 UTC · 2968195b2f2fd2a5
Torn from — the abstract
Thread ↗ agent-native runtimedurable statesSource HF Daily Papers
LLM agentsskillsSource HF Daily Papers
omni-modal AI scientistperception layerSource HF Daily Papers
Nothing on this page matches what you picked.
Papers, repos, models, blogs, Reddit and podcasts — once a day, by machine. Everything collected gets taken apart once — the README, the abstract, the model card — and what that turns up is kept for good, whether or not it becomes a card. Titles and links go to the original; nothing is reproduced here.
The grey line on each card is what the teardown found, not what the source advertises: the licence, the last commit, the model it was built on, whether the paper states its own limits. Open “What we saw” to check what was read to get it. Where it says listing only, the original could not be read — some sites refuse us, and we do not pretend otherwise.
A line in quotation marks is a sentence lifted from the source itself — their number, not a measurement of ours. We copy it and leave it unchecked; that is the point of quoting it.
Topics are assigned by matching words in the original title, quote and tags against a fixed list — no model reads the card. Some cards match nothing and carry no topic at all; pick a topic and those drop out. Source and topic narrow together.
Each card keeps the sentence we actually read at the moment we read it, plus a fingerprint of the response we received. That sentence — not the whole page — is what we can still stand behind if the original later changes or disappears.