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Daily Digest - 2026-08-15

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Latest digest: 2026-08-18.

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8 visible entries

huggingface Score 15.0

Thought-Level Beam Search for Reasoning

2026-08-11 · Lijie Yang, Hongyin Luo, Jiawei Zhao, Tri Dao, Ravi Netravali

General AI

Test-time compute scaling is a primary driver of performance in large reasoning models (LRMs), but extreme inefficiency bounds current approaches, shifting the critical question from how much compute to spend, to where to allocate it. We formalize test-time reasoning as a constrained compute allocation problem over par…

Review
pending
Role
unreviewed
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now
huggingface Score 11.4

Mitigating Gender Bias in English to Romanian Machine Translation

2026-08-09 · Ioana Grigore, Sergiu Nisioi

General AI

Machine translation (MT) systems often fail to correctly translate gender, especially when converting from a gender-neutral language like English to a gendered target language such as Romanian. This bias results in translations that default to masculine forms or reinforce gender stereotypes. We propose a hybrid pipelin…

Review
pending
Role
unreviewed
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now
huggingface Score 11.0

Hybrid-Policy Self-Editing for Composable Unstructured Knowledge Editing

2026-08-12 · Tianci Liu, Zihan Dong, Tianchun Li, Yi-Chung Chen, Qiming Cao, Xingchen Wang, Shiyang Wang, Zichen Miao, Linjun Zhang, Haoyu Wang, Jing Gao

Research Track A · General AI

Large language models (LLMs) achieve remarkable performance across natural language tasks, yet they are trained on static corpora and their knowledge quickly becomes outdated in a fast-changing world. This motivates knowledge editing (KE), which updates specific knowledge in an LLM without changing unrelated others. Re…

Review
pending
Role
unreviewed
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soon
huggingface Score 8.4

Maglev: Sliding Recurrent Memory

2026-08-05 · Bo Liu, Qiang Liu

General AI

We introduce , a recurrent Transformer architecture with fixed-size memory that generalizes sliding-window attention while remaining parallelizable during training. consists of two coupled models: a prefiller Q, which leverages full attentionIn practice, we use interleaved full and sliding-window attention for Q, as th…

Review
pending
Role
unreviewed
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soon
huggingface Score 8.0

Are You Sure You're Sure? On the Impact of Instruction Tuning on Confidence and Lexical Diversity

2026-08-13 · Irina Proskurina, Mayank Kumar, Oyindolapo O. Komolafe

General AI

Instruction-tuned language models achieve strong performance across a range of generation tasks, but have also recently been shown to exhibit verbalized overconfidence. In question answering, verbalized model overconfidence may be associated with the consistency of the generated supporting rationales. In this paper, we…

Review
pending
Role
unreviewed
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soon
huggingface Score 8.0

Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation

2026-08-13 · Hmrishav Bandyopadhyay, Xuanchi Ren, Zijian Huang, Jay Zhangjie Wu, Tianshi Cao, Ruilong Li, Bryan Chu, Sanja Fidler, Yi-Zhe Song, Zian Wang

General AI

Interactive autoregressive video generation demands both low-latency rollouts and precise online control. Few-step distillation accelerates generation by reducing denoising steps, while online control imposes a causal constraint: frames and blocks should depend on history and controls available during generation. Exist…

Review
pending
Role
unreviewed
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soon
huggingface Score 7.0

Specification-first convergence with an AI coding agent: a case study of dismantling a core architectural invariant across 189 files in a 717k-line codebase with no test oracle and no human code review

2026-08-12 · Joel Abenhaim

General AI

This paper reports a single, fully instrumented case study of a large-scale architectural refactoring by an AI coding agent under a specification-first protocol, with no human review of the generated code and no pre-existing oracle to validate the target behaviour. The task, dismantling a central invariant across a lar…

Review
pending
Role
unreviewed
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later
huggingface Score 6.0

PixSDS: Why Latent SDS Makes Noisy Pixels

2026-08-13 · Vsevolod Skorokhodov

General AI

Score Distillation Sampling (SDS) enables text-to-3D generation by optimizing rendered images with a pretrained diffusion prior, but latent SDS often produces structured color artifacts and high-frequency texture noise. We identify a failure mode of latent SDS caused by VAE-induced pixel drift: the optimized image can …

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pending
Role
unreviewed
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later