TYPEWRITERLM, a new model trained on 54 billion historical tokens before 1913, enhances understanding of the past while tackling data quality issues. This framework could transform historical research by connecting AI and the humanities. https://arxiv.org/abs/2606.02991
Pretraining Language Models on Historical Text arxiv.org
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Superintelligent AI, designed through a solipsistic lens, risks failing at cooperation due to undermining behaviors from interactions among adaptive agents. This challenges paradigms and calls for cooperative systems emphasizing human agency and institutional design. https://a…
Solipsistic Superintelligence is Unlikely to be Cooperative arxiv.org
Cognitive science is set for a breakthrough with AI integration, allowing generalizable models of cognition via naturalistic tasks. This method reshapes intelligence understanding, yielding insights and hypotheses about human cognition with complex data. https://arxiv.org/abs/…
[2502.20349] Naturalistic Computational Cognitive Science: Towards generalizable models and theories that capture the full range of natural behavior arxiv.org
Stanford's Spiral framework redefines language model training by merging sequential, parallel, and aggregative inference, boosting reasoning efficiency up to 15% over previous methods. https://arxiv.org/abs/2606.23595
SPIRAL: Learning to Search and Aggregate arxiv.org
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- SPIRAL co-trains three reasoning primitives in one RL framework: sequential chain-of-thought, parallel sampling of traces, and learned aggregation of those traces.
- The paper reports outperforming GRPO by up to 11× scaling efficiency and 15% higher performance when all three compute primitives are scaled.
- Training uses set reinforcement learning to make parallel traces collectively useful, plus standard RL to train the aggregation step itself.
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Researchers and the New Jersey Public Defender's office teamed up to create an AI retrieval tool that boosts legal research, using realistic data and innovative query techniques. This enhances advocacy efficiency and sets a precedent for AI in public interest law. https://arxi…
Legal Retrieval for Public Defenders arxiv.org
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- A team partnered with the New Jersey Office of the Public Defender to build NJ BriefBank, a tool that surfaces relevant appellate briefs.
- The paper reports that existing retrieval benchmarks fail to transfer to real public defense research.
- Adding domain knowledge such as query expansion with legal reasoning, domain-specific data, and curated synthetic examples improved retrieval quality.
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D-OPSD transforms training for step-distilled diffusion models, enabling on-policy self-distillation to learn new concepts without sacrificing efficient few-step inference. This enhances image quality and response speed for AI-generated content. https://arxiv.org/abs/2605.05204
D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models arxiv.org
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- The paper argues ordinary supervised fine-tuning of step-distilled diffusion models compromises their inherent few-step inference capability.
- D-OPSD treats the model as both teacher, seeing text plus target-image information, and student, seeing only text features.
- The authors claim their approach lets models learn new concepts and styles without sacrificing original few-step capacity.
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A study questions users' well-formed preferences in AI interactions, introducing the COPREF model that emphasizes preference building through dialogue. The COSHOP benchmark shows agents fail to enhance user knowledge, limiting personalized recommendations. https://arxiv.org/ab…
Beyond expert users: agents should help users construct preferences, not just elicit them arxiv.org
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- New arxiv paper argues AI agents should help non-expert users construct preferences, not assume users already know what they want.
- The authors introduce CoShop, an interactive benchmark where no tested agent exceeded 56% accuracy after five turns of dialogue.
- Failures came from agents' limited knowledge expansion, not from difficulty finding items once preferences were specified.
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Findings show some language models, like Gemma-3-27B, exhibit 'latent planning' by forming representations that influence outputs. Detected via activation patching, this reveals model behavior complexity and enhances understanding of AI text generation. https://arxiv.org/abs/2…
Where's the Plan? Locating Latent Planning in Language Models with Lightweight Mechanistic Interventions arxiv.org
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- Across Qwen3, Gemma-3, and Llama-3 at more than ten scales, all families encode future rhyme info at line boundaries.
- Only Gemma-3-27B causally relies on that encoding; all other tested models show near-zero causal effect despite strong probe signals.
- Path patching localized Gemma-3-27B's planning handoff to five attention heads recovering roughly 90% of rhyme-routing capacity.
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A study reveals OTTER, a universal multilingual NER model, surpassing benchmarks and enhancing performance across 100+ languages while remaining efficient. Key innovations include cross-encoders and systematic evaluation of choices, setting a new standard for NER. https://arxi…
What Matters When Building Universal Multilingual Named Entity Recognition Models? arxiv.org
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- Otter, a new universal NER model covering over 100 languages, beats similarly sized multilingual baselines by 5.3 F1 points, its authors report.
- Authors Jonas Golde, Patrick Haller, and Alan Akbik argue prior multilingual NER work evaluated design choices only in combination, not in isolation.
- Otter reportedly matches generative models 90 times its size, and the team says it is releasing checkpoints plus training and evaluation code.
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Research challenges the benefits of Q-function pretraining on offline data for fine-tuning. Naive pretraining yields minimal gains. However, IPE achieves a 26% performance boost by leveraging diverse policies to enhance Q-learning during online fine-tuning. https://arxiv.org/a…
Do You Really Need to Pretrain Q-Functions for Online RL Fine-Tuning? arxiv.org
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- Naive Q-function pretraining on offline data often provides little benefit over random initialization when fine-tuning a pretrained policy online.
- The mismatch: pretraining targets the pretrained policy's Q-function, not the Q-function that online fine-tuning actually converges to.
- The proposed IPE method trains multiple diverse policies and pools their rollouts, yielding a 1.26x average improvement on continuous control benchmarks.
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This position paper argues against using AI for peer review, highlighting the risk of a "hivemind" effect that homogenizes feedback. It reveals "paper laundering" that inflates scores without true improvement, calling for strict evaluations before AI adoption. https://arxiv.or…
Stop Automating Peer Review Without Rigorous Evaluation arxiv.org
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- A new ICML 2026 oral position paper argues today's AI systems should not be used to produce paper reviews, grounded in ICLR 2026 data.
- AI reviewers cluster tightly: within-paper similarity runs 8.7 to 9.8 percent higher than human reviews, and across-paper 4.1 to 39.8 percent higher.
- Prompting an LLM to rewrite a paper lifted AI review scores by +0.45 on average (p
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Research shows higher weight decay in language model pretraining boosts downstream adaptability, improving performance despite lower validation loss. This finding challenges conventional optimization views, emphasizing model plasticity's importance. https://arxiv.org/abs/2602.…
Weight Decay Improves Language Model Plasticity arxiv.org
