The Archive

Hi everyone, I set up a Claude Project a few weeks ago, but the status indicator in the top right corner has never changed from "Indexing" and still shows that little black dot. Does this status ever clear up once processing finishes? Also, my larger PDFs aren't showing visual thumbnails like the smaller files do. The cards just show the total number of lines in the text. Does this indicate a processing failure, and would splitting these large documents into smaller files help clear the indexing queue? Thanks for any and all suggestions.

Google Gemini Omni demonstrates strong video manipulation capabilities, highlighting a key technical strength of the multimodal model.

Reddit post expressing support for Google Gemini Omni without substantive technical details or benchmarks.

Nathan Witkin critiques METR's Long Tasks benchmark methodology, identifying severe flaws in the widely-cited AI time horizons graph.

Reddit user reports account suspension after minimal Claude usage for academic research assistance on Parkinson's Disease methodology.

Reddit user shares personal anecdote about health impacts from intensive Claude use and wellness practices adopted.

MobileGym enables parallel RL training for mobile GUI agents via deterministic JSON-based state representation, supporting hundreds of concurrent instances.

Paper argues agentic AI bottleneck shifts from model to system scaling; proposes 'scaling the harness'—designing auditable, modular architectures around foundation models.

Method conditions diffusion models on MLLMs for subject-driven image generation, improving identity preservation and instruction following via joint text-image encoding.

Prism: plug-in infrastructure for multimodal continual instruction tuning of MLLMs, addressing engineering bottlenecks via modular method-agnostic design.

LoopMDM: selective layer looping in masked diffusion models improves training efficiency and performance, enabling depth scaling without parameter increase.

LLM-based framework for structured code change labeling (renames, moves, logic edits) to improve code review efficiency beyond summarization.

Sleep-like consolidation mechanism converts context to fast weights in SSM blocks, reducing attention's poor scaling with long context in LLMs.

Self-generated replay data from LMs nearly eliminates catastrophic forgetting on new tasks; forgetting persists only when model capacity is exhausted.

Reddit user reports billing issues and unresponsive support from Anthropic; anecdotal customer service complaint.

GoBOED: goal-driven Bayesian experimental design framework optimizing information gathering for decision-critical objectives under model uncertainty.

OrpQuant: geometric orthogonal residual projection for power-of-two quantization enabling multiplier-free LLM/ViT deployment on edge devices.

I've fine-tuned Qwen 3.5 0.8B on the dataset provided by Pangram with their EditLens paper. It's available via a [Chrome extension](https://chromewebstore.google.com/detail/slop-hammer/gfjdmhfokmhedlgfggmmgchpppmhkdgg); you can just click selected text and it's going to give you the probability distribution of how likely it is AI-generated. It takes under 1s on my M1 MacBook Pro. Pangram did release Llama 3.2 3B trained on their dataset, but I found this model slightly too legacy (too big for the capabilities). Qwen 0.8B (base) ended up being as good after roughly 20h of fine-tuning on a sin...

Channel-wise Vector Quantization replaces patch-wise image tokenization with channel-wise quantization for autoregressive visual models.

DiscoverPhysics benchmark tests LLM reasoning by having agents discover physics laws in simulated worlds with non-standard dynamics.

Claw-Anything benchmark evaluates LLM agents as always-on assistants with access to long-horizon histories and interdependent backend services.

VeriTrace proposes explicit feedback mechanisms for research agents to evolve mental models and prevent error propagation in uncertain information.

Auto Benchmark Audit uses agentic framework to systematically audit AI benchmarks for hidden dependencies, specification gaps, and grading flaws.

Wasserstein Policy Gradient for entropy-regularized RL proves global convergence using optimal-transport geometry for continuous control.

StakeBench evaluates financial language understanding using real market commitments from 560K+ Polymarket and Manifold comments instead of human labels.

Active Query Synthesis for preference learning uses confidence-aware response model to reduce labeling cost for pairwise comparisons.

WhoSaidIt applies human-LLM collaborative re-annotation to stabilize multilingual speaker-attribute labels via disagreement-focused sampling.

WSADBench unifies weakly supervised anomaly detection evaluation across incomplete, inexact, inaccurate supervision for 36 algorithms and 4 modalities.

Developer shares prompt-based skills to make Claude challenge product ideas rather than automatically validate them via custom instructions.