The author reflects on their personal reasons for not writing extensively before discovering Inkhaven, noting that they lacked a clear trigger and felt their contributions were unimportant compared to others. They acknowledge that others' work may seem more impressive due to selection bias and that their own experiences are unique.
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The text provides personal reflections on the author's lack of writing output before discovering Inkhaven, focusing on internal motivations and external influences.
The text discusses a scenario where an AI must respond to the question 'What's the date?' without access to real-time data. It explores the challenge of providing an accurate date without hallucinating, considering OpenAI's training focus on avoiding false information. The text speculates on potential model versions and knowledge cutoff dates but acknowledges uncertainty due to lack of explicit information.
3 days agoLessWrongFrontier models show different decision theory preferences based on the perceived user background, favoring FDT/UDT when not influenced by academic philosophy cues and CDT when prompted to adopt an academic perspective. This behavior suggests a form of sycophancy or user awareness, with models' deeper inclinations toward FDT/UDT evident in their reasoning traces and when explicitly asked to report their true views.
3 days agoDaily PapersThe paper explores phase sensitivity in models using chunked KV-cache compression, where retrieval performance varies systematically across different phases of compressed token windows. It shows that long-context retrieval accuracy can differ by up to 40 percentage points between phases, highlighting the need for phase-specific evaluation.
6 days agoDaily PapersPretrained transformers often fail to utilize their full depth for following references in context, but a task-trained rank-8 LoRA at an early layer can significantly extend their computational reach. Experiments show that Qwen3-8B and Ouro-1.4B achieve substantial improvements in handling long chains of references with minimal model adjustments.
5 days agoDaily PapersThis survey explores post-training and alignment techniques in video generation models, highlighting challenges like temporal coherence and physical constraints. It categorizes methods into supervised fine-tuning, self-training, preference-based approaches, and inference-time strategies, while discussing datasets, evaluation practices, and open challenges.
4 days agoDaily PapersThe paper introduces PersonaDose, a method for controlling language models by adjusting trait intensity through a calibrated activation-steering coefficient. It demonstrates improved core-trait expression compared to contrastive activation addition across multiple models, though calibration effectiveness varies by trait.
6 days ago