Jon Udell: Human Agent in the loop
Jon Udell argues against the phrase "human in the loop" because it cedes authority to machines, proposing instead that humans should invite agents into their existing workflows as team members.
Neofold, the idle creature-collector with infinite pets thanks to a local diffusion model, released this week
Neofold is an idle creature-collector game that utilizes a local diffusion model to generate an infinite variety of pets. The title was recently released and is available on Steam.
Diffusion Models Adapt to Low-Dimensional Structure Under Flexible Coefficient Choices
This paper demonstrates that diffusion models' ability to exploit low-dimensional structure for accelerated sampling is a robust property independent of specific update coefficient choices. The authors prove that a broad class of coefficients allows generating an ε-accurate sample in O(k/ε) iterations, regardless of ambient dimension.
Dynamic estimation of slowly varying sequences
This article introduces a framework for sequentially approximating functions in slowly-varying sequences, leveraging the reuse of past queries to reduce overall computational cost. The authors present novel sequential estimation results for matrix powers, spectral densities, Monte Carlo integration, and partial differential equation boundary value problems.
Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives
The article introduces Action-BED, a new framework for Bayesian experimental design that formulates the problem in terms of expected future loss on downstream actions rather than uncertainty reduction. This approach converts traditionally doubly intractable objectives into singly intractable ones that can be jointly optimized using stochastic gradients.
MAS-PromptBench: When Does Prompt Optimization Improve Multi-Agent LLM Systems?
This study systematically investigates the impact of system-prompt optimization on multi-agent systems (MAS) by benchmarking two optimizers across diverse configurations of tasks, workflows, and team sizes.
On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners
This paper argues that Large Language Models are not universal problem solvers through prompting alone, due to fundamental constraints in language as a communication interface and alignment requirements. The authors analyze user-system interaction as a cheap-talk game to derive PAC-Bayes bounds distinguishing estimation error from structural limitations.
Tapered Language Models: Improving Performance via Depth-Aware Capacity Allocation
The article introduces Tapered Language Models (TLMs), an architectural principle that allocates more parameter capacity to earlier layers and less to later layers within a fixed budget. This approach challenges the standard practice of uniform layer width by leveraging evidence that later layers primarily refine the residual stream rather than transforming it.
PsyBridge: A Hybrid Intelligent Framework for Multi-Dimensional Mental Health Assessment
This study introduces PsyBridge, a hybrid intelligent framework designed to address the limitations of isolated mental health screening tools by integrating clinically validated assessments with cognitive and personality profiling. The system utilizes a modular architecture and weighted aggregation mechanism to generate interpretable risk classifications and decision support recommendations.
Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?
This article addresses the lack of rigorous convergence theory for the AdamW optimizer in regimes with heavy-tailed stochastic gradient noise, which is common in large language model pretraining. It questions whether AdamW can converge under these conditions or if its second-moment accumulator creates a genuine obstruction.
Semantic Browsing: Controllable Diversity for Image Generation
This article introduces Semantic Browsing, a method for generating controlled diversity in text-to-image models by enforcing structure on generated samples to overcome the lack of meaningful variation in current systems. The approach induces diversity directly at the text level rather than relying on stochastic variations within the model.
User implements C++ tool execution with MiMo-V2.5-GGUF
A user successfully utilized the MiMo-V2.5-GGUF model to write a built-in llama.cpp tool for executing C++ code and retrieving results. The implementation was achieved using opencode, where the model generated the necessary code based on specific instructions.
Why so many trash fine-tuned models on HuggingFace?
The author observes that the majority of fine-tuned models uploaded to Hugging Face perform worse than their base counterparts, rendering them useless. This proliferation is attributed to individuals using these models as a form of professional credentialing to secure high-paying AI positions.
llama.cpp b9835 release with UI stop and reasoning skip fixes
The llama.cpp project has released version b9835, which includes a fix for the stop and reasoning skip functionality in single-model mode. This update addresses specific issues within the user interface to improve control during model inference.
Script to monitor llama cpp and analyze memory usage
A user has shared a Bash script designed to parse the verbose output of llama.cpp, providing a clear summary of VRAM/RAM requirements and runtime performance metrics. This tool addresses the difficulty of predicting memory needs for various model quantizations by grouping buffer allocations by function and backend.
Ornith-1.0-35B GGUF update: native MTP speculative-decode graft + full serving/TTFT/long-context numbers (llama.cpp, tp=1)
This article reports on an update to the Ornith-1.0-35B model, featuring a native MTP draft head grafted onto the IQ4_XS body for self-speculative decoding in llama.cpp. The author provides comprehensive performance metrics including throughput, time-to-first-token (TTFT), and long-context capabilities on a single RTX PRO 6000 Blackwell GPU.
Apple Refurbished Adds M5 Pro and Max Options
Following Apple's recent price increase, the company has added numerous top-of-the-line 14-inch MacBook Pro models equipped with M5 Pro and M5 Max chips to its refurbished store.
China Has Matched Anthropic in Cybersecurity, Resetting AI Race
A Wall Street Journal report indicates that Chinese artificial intelligence models have achieved parity with Anthropic's Claude in cybersecurity tasks.
Reddit user refutes Dario Amodei's claims against open-source AI
A Reddit post challenges Dario Amodei's assertion that open-source models are inferior to proprietary systems by arguing he misunderstands the technology. The author contends that Amodei is unaware of the transparency and capabilities of current open-weight models.
Hypothetical Inquiry on AI Learning Binary Code
A forum user poses a speculative question regarding whether training neural networks or AI systems to understand binary code would significantly enhance their overall capabilities, particularly in coding tasks.