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Papers

Page 3 of 4

These are earlier cards, kept as text. Pictures and short summaries stay only while a card is on the first page; the link goes to the original either way.

Source HF Daily Papers
ExplorationBench: Measuring AI Systems' Exploration in Verifiable Alien Worlds

12 upvotes1 comments

20 authorsstates limits

What we saw

“Scientific discovery begins where known problems end. There, AI systems must engage in exploration: framing hypotheses, designi…”

They claim — We evaluate 10 AI systems and find that the strongest systems can acquire and apply unfamiliar rules, while performance varies substantially across trajectories and continued exploration …

Published 2026-09-23

Fetched 2026-09-25 23:01 UTC · 9ccf356c7f3c30e0

Torn from — the abstract

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Source HF Daily Papers
Rufus-Air: An Open LLM Post-Training Recipe

19 upvotes3 comments

22 authorsno limits stated

What we saw

“Rufus-Air is an open and reproducible post-training recipe on GLM-4.5-Air-Base (106B-A12B), organized as a serial pipeline of e…”

They claim — Rufus-Air improves over the official GLM-4.5-Air post-trained release and is competitive with similarly sized open models.

Published 2026-09-23

Fetched 2026-09-25 23:01 UTC · ecf373efc650dcaf

Torn from — the abstract

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Source HF Daily Papers
Learning to Discover Interesting Mathematics

12 upvotes2 comments

5 authorsno limits stated

What we saw

“Recently, Large Language Models (LLMs) have been increasingly able to solve advanced mathematical problems, including many that…”

They claim — Optimizing for our metric creates a model capable of producing more interesting theorems, while also reducing substantial or full overlap with Mathlib from 91.9% to 30.6%, showcasing the …

Published 2026-09-22

Fetched 2026-09-25 23:01 UTC · 6d69919b7b03eed9

Torn from — the abstract

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Source HF Daily Papers
Training Object Permanence in World Models

206 upvotes2 comments

31 authorsno limits stated

What we saw

“Object permanence and solidity are hallmarks of human cognitive priors. Recent studies show that video generation models, a par…”

Published 2026-09-22

Fetched 2026-09-25 23:01 UTC · 9ddf4b0029a949c3

Torn from — the abstract

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Source HF Daily Papers
OmniEcho: Spatial Audio Understanding for Embodied Agents

23 upvotes2 comments

13 authorsno limits stated

What we saw

“Humans can effortlessly localize the direction of a sound source and integrate it with visual cues for reasoning, yet this rema…”

Published 2026-09-19

Fetched 2026-09-25 23:01 UTC · ebf4594fa0d2f86a

Torn from — the abstract

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Source HF Daily Papers
AI for Games in the Foundation Model Era

106 upvotes1 comments

10 authorsno limits stated

What we saw

“Foundation models, alongside advances in learned game-world models, are reshaping AI across the game lifecycle. Beyond playing …”

Published 2026-09-14

Fetched 2026-09-16 23:01 UTC · bcd23ad210dcebdc

Torn from — the abstract

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vision-language modelautoregressive next-token predictionSource HF Daily Papers
PhysBrain 1.5: From Vision-Language Models to Physical Foundation Models

172 upvotes3 comments

vision-language model54 authorsno limits stated

What we saw

“PhysBrain 1.5 unifies physical environment understanding, action generation, and future state prediction via joint autoregressi…”

They claim — Across 28 embodied understanding benchmarks, our 8B model achieves an average score of 72.5, setting a new open-source state of the art and performing on par with leading proprietary mode…

Published 2026-09-13

Fetched 2026-09-15 23:01 UTC · 70061e1a1aeb68ea

Torn from — the abstract

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recursive self-improvementexploration strategiesSource HF Daily Papers
Dream-RSI: Recursive Self-Improvement through Evolving Worlds

278 upvotes3 comments

recursive self-improvement17 authorsstates limits

What we saw

“Dream-RSI enables scalable recursive self-improvement by using historical discovery replay to evaluate exploration policies off…”

Published 2026-09-13

Fetched 2026-09-15 23:01 UTC · 263aacf523ded915

Torn from — the abstract

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foundation agentic language modelVerifiable Experience PipelineSource HF Daily Papers
Atria Dawn: The Dawn of Agentic Superintelligence

424 upvotes3 comments

foundation agentic language model143 authorsno limits stated

What we saw

“Atria Dawn Preview is a foundation agentic language model trained through verified tool interactions that achieves strong bench…”

They claim — Across 16 benchmarks spanning real-world research, engineering, and digital work, Atria Dawn Preview is competitive with frontier agents and achieves the highest reported score on five of…

Published 2026-09-13

Fetched 2026-09-15 23:01 UTC · 21c391716120f827

Torn from — the abstract

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Source HF Daily Papers
StepAudio 3 Realtime Technical Report

91 upvotes1 comments

90 authorsno limits stated

What we saw

“Realtime spoken interaction demands deep reasoning, prompt responses, and fluid turn-taking. We present StepAudio 3 Realtime, a…”

They claim — StepAudio 3 Realtime achieves top-tier performance across key dimensions: an exceptional 90.6 on the MMSU benchmark, 98.9 Overall on the Artificial Analysis Full-Duplex Bench, and a 56.0%…

Published 2026-09-11

Fetched 2026-09-16 23:01 UTC · c13c53fb82422478

Torn from — the abstract

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vision-action shortcutslatent interface trainingSource HF Daily Papers
Breaking the Vision-Action Shortcut: Latent Interface Training for Generalizable Robotics Foundation Models

71 upvotes2 comments

vision-action shortcuts4 authorsno limits stated

What we saw

“LIT improves robot action generalization by first training pose-conditioned action priors without images, then constraining vis…”

They claim — Across four vision-language-action and world-action architectures (Pi0.5, MolmoAct2, FAST-WAM, and ImageWAM), LIT improves overall LIBERO-Plus success by 3.87-10.70 percentage points whil…

Published 2026-09-10

Fetched 2026-09-14 23:01 UTC · 3e83d75847ca11ec

Torn from — the abstract

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gated sliding-window attentionfull attentionSource HF Daily Papers
ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search

302 upvotes7 comments

gated sliding-window attention22 authorsstates limits

What we saw

“ZGCM-1 is a 7B open foundation model that combines internal reasoning with external tool use, trained via efficient architectur…”

They claim — On several challenging mathematical reasoning and agentic search suites, it remains competitive with frontier models orders of magnitude larger, such as Qwen3-235B-A22B and GLM-5.1.

Published 2026-09-10

Fetched 2026-09-15 23:01 UTC · fcb1e94d9a32e870

Torn from — the abstract

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real-time video generationspatial video generationSource HF Daily Papers
Vidu S2: Real-Time Interactive, Editable, and Spatial Video Generation

695 upvotes2 comments

real-time video generation35 authorsno limits stated

What we saw

“Vidu S2 introduces real-time interactive avatar and video editing models that support high-resolution spatial video generation …”

They claim — Experiments show that Vidu S2 outperforms all baselines.

Published 2026-09-09

Fetched 2026-09-15 23:01 UTC · 5e45abe94152d970

Torn from — the abstract

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post-trainingsupervised fine-tuningSource HF Daily Papers
Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models

130 upvotes3 comments

post-training7 authorsno limits stated

What we saw

“A data-centric framework with specialized systems for reasoning analysis, cost reduction, and execution verification enables sm…”

They claim — The three checkpoints improve over their starting models by an average of 23.76% on the full CyberGym suite and 10.49% across the pooled CTF suites.

Published 2026-09-07

Fetched 2026-09-14 23:01 UTC · 4d29dafb2e864139

Torn from — the abstract

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catastrophic forgettingcontinual supervised fine-tuningSource HF Daily Papers
Continual Learning Mechanisms Compose for Long-Horizon Memorization

287 upvotes3 comments

catastrophic forgetting4 authorsno limits stated

What we saw

“Composing complementary continual learning mechanisms substantially improves long-horizon memorization in language models by co…”

They claim — Our best method combines all three anchors with merged LoRA, ranks among the top 3 methods in all datasets, and raises average final retention from 1.2% under naive sequential fine-tuning…

Published 2026-09-06

Fetched 2026-09-16 23:01 UTC · 299fad04b14317fa

Torn from — the abstract

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RLVRGRPOSource HF Daily Papers
DataFlex-RL: An Evaluation Platform for RLVR Data Policies

110 upvotes2 comments

RLVR5 authorsstates limits

What we saw

“DataFlex-RL evaluates reinforcement learning data policies and finds that uniform sampling matches or exceeds adaptive rollout …”

They claim — Uniform GRPO improves the domain-balanced average accuracy by 7.76 percentage points over the untrained checkpoint.

Published 2026-09-04

Fetched 2026-09-14 23:01 UTC · 8b008970febe99b9

Torn from — the abstract

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cs.AISource arXiv
Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference

17 upvotes2 comments

cs.AI9 authorsno limits stated

What we saw

“Layer dropout (a.k.a. stochastic depth) has been shown to enable faster training, higher accuracy, and robustness to zero-shot …”

They claim — For a given number of training steps, LLMs can achieve lower or similar validation loss while saving upto 25% of training FLOPs.

Published 2026-09-04

Fetched 2026-09-07 01:32 UTC · 60c554528e9aa388

Torn from — the abstract

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compositional 3D representationobject meshesSource HF Daily Papers
WorldSculpt: Generating Compositional Worlds from Grounded Videos

21 upvotes1 comments

compositional 3D representation12 authorsno limits stated

What we saw

“Adapting a single-object 3D generative prior to multi-view observations enables scalable compositional mesh reconstruction of d…”

Published 2026-09-03

Fetched 2026-09-07 23:01 UTC · acfc206edb29736d

Torn from — the abstract

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topology-aware diffusion transformergraph-aware attention biasSource HF Daily Papers
UniMate: One Unified Model to Animate Diverse Skeletons

13 upvotes2 comments

topology-aware diffusion transformer7 authorsno limits stated

What we saw

“UniMate is a unified diffusion transformer that generates articulated motion for arbitrary skeletons from text and rigged 3D as…”

They claim — UniMate introduces a topology-aware diffusion transformer, which integrates skeletal topology into attention via three mechanisms: (1) a graph-aware attention bias from pairwise joint rel…

Published 2026-09-03

Fetched 2026-09-07 23:01 UTC · f7aead1ac8cf4d5a

Torn from — the abstract

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search agentsmulti-hop chainsSource HF Daily Papers
Iris: Climbing to the Search Frontier

53 upvotes4 comments

search agents9 authorsno limits stated

What we saw

“Two large-scale search agents are trained via a multi-stage pipeline combining supervised fine-tuning and reinforcement learnin…”

Published 2026-09-02

Fetched 2026-09-07 23:01 UTC · bf933da181c11d7a

Torn from — the abstract

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audio-video diffusion modelscross-modal attentionSource HF Daily Papers
The Attention Triangle in Audio-Video Models

29 upvotes3 comments

audio-video diffusion models7 authorsno limits stated

What we saw

“Audio-video diffusion models exhibit bidirectional semantic leakage through cross-modal attention pathways, which can be diagno…”

Published 2026-09-02

Fetched 2026-09-07 23:01 UTC · 496e910644d364a4

Torn from — the abstract

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text-promptable segmentationSAMSource HF Daily Papers
ENEAS: Embedding-guided Neural Ensemble for Adaptive Segmentation

33 upvotes2 comments

text-promptable segmentation5 authorsstates limits

What we saw

“ENEAS unifies text-prompted instance tracking and open-concept semantic discovery via temporal memory extension and a verificat…”

Published 2026-09-02

Fetched 2026-09-08 16:21 UTC · a5384a599ed5599b

Torn from — the abstract

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FlowBalanceon-policy self-improvementSource HF Daily Papers
FlowBalance: Verifier-Grounded Self-Improvement from On-Policy Reasoning Experience

73 upvotes1 comments

FlowBalance4 authorsno limits stated

What we saw

“FlowBalance improves reasoning models via verifier-calibrated self-guidance using trajectory-level score reweighting and profil…”

They claim — On mathematical reasoning, FlowBalance improves average performance over FlowRL on both Qwen3-4B and Qwen3-8B, while also improving training speed and stability, avoiding direct OPSD's re…

Published 2026-09-02

Fetched 2026-09-08 16:21 UTC · 437efcb18b365a17

Torn from — the abstract

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Open Information Extractionmulti-level hallucination detectionSource HF Daily Papers
Enoki: Efficient Multi-Level Hallucination Detection

21 upvotes2 comments

Open Information Extraction8 authorsno limits stated

What we saw

“Enoki is an open information extraction framework that unifies claim-level verification and span-level hallucination localizati…”

Published 2026-08-31

Fetched 2026-09-07 23:01 UTC · ab91839c3bae8764

Torn from — the abstract

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Source HF Daily Papers
Dr. Claw: An AI Scientist Workspace for Vibe Research

124 upvotes3 comments

13 authorsno limits stated

What we saw

“Command-line coding agents (e.g., Claude Code, Gemini CLI) can already read and write files and sustain long sessions, yet end-…”

Published 2026-08-30

Fetched 2026-09-07 23:01 UTC · 17101143ec4ae6bc

Torn from — the abstract

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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.