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Bifocal Diffusion Language Models: Asymmetric Bidirectional Context for Parallel

BACKGROUND
3 SIGNALSFIRST DETECTED 30 June 2026
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1 Oct
26 Aug
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Other feeds and industry voices
8 May
Predict-then-Diffuse: Adaptive Response Length for Compute-Budgeted Inference in Diffusion LLMs

arXiv:2605.04215v1 Announce Type: cross Abstract: Diffusion-based Large Language Models (D-LLMs) represent a promising frontier in generative AI, offering fully parallel token

REPORT · WHAT · ArXiv CS.AIOPEN STORY
27 Apr
Focus Session: Hardware and Software Techniques for Accelerating Multimodal Foundation Models

arXiv:2604.21952v1 Announce Type: cross Abstract: This work presents a multi-layered methodology for efficiently accelerating multimodal foundation models (MFMs). It combines

REPORT · WHAT · ArXiv CS.AIOPEN STORY
20 Mar
SpecForge: A Flexible and Efficient Open-Source Training Framework for Speculative Decoding

arXiv:2603.18567v1 Announce Type: cross Abstract: Large language models incur high inference latency due to sequential autoregressive decoding. Speculative decoding alleviates

REPORT · HOW · ArXiv CS.AIOPEN STORY
20 May
Generative Recursive Reasoning

arXiv:2605.19376v1 Announce Type: new Abstract: How should future neural reasoning systems implement extended computation? Recursive Reasoning Models (RRMs) offer a promising

REPORT · HOW · ArXiv CS.AIOPEN STORY
9 Apr
HybridKV: Hybrid KV Cache Compression for Efficient Multimodal Large Language Model Inference

arXiv:2604.05887v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have advanced unified reasoning over text, images, and videos, but their inference is

REPORT · WHAT · ArXiv CS.AIOPEN STORY
27 Apr
Estimating Tail Risks in Language Model Output Distributions

arXiv:2604.22167v1 Announce Type: cross Abstract: Language models are increasingly capable and are being rapidly deployed on a population-level scale. As a result, the safety of

REPORT · WHEN · ArXiv CS.AIOPEN STORY
13 Apr
Re-Mask and Redirect: Exploiting Denoising Irreversibility in Diffusion Language Models

arXiv:2604.08557v1 Announce Type: cross Abstract: Diffusion-based language models (dLLMs) generate text by iteratively denoising masked token sequences. We show that their safety

REPORT · WHAT · ArXiv CS.AIOPEN STORY
14 Apr
MEMENTO: Teaching LLMs to Manage Their Own Context

arXiv:2604.09852v1 Announce Type: new Abstract: Reasoning models think in long, unstructured streams with no mechanism for compressing or organizing their own intermediate state.

REPORT · HOW · ArXiv CS.AIOPEN STORY
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