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Full-fabric VHDL LLM inference engine. Runs Qwen3.5-class transformer inference
FPGA fabric runs Qwen3.5 at 9B parameters with no GPU in sight A project called llm.vhdl has built a complete large language model inference engine in VHDL that runs on Xilinx Versal FPGAs. The design executes Qwen3.5-class models...
Embedding Prediction Helps Image Generation
LLMs are increasingly applied to cybersecurity workflows, where they are expected to translate analysts' intent into tool invocations. However, existing evaluations focus on knowledge-based assessments or end-to-end agentic tasks,...
Cost-augmented Schrödinger bridges on graphs are exactly solvable: a Feynman-Kac tilt replaces learned control
The generalized Schrödinger bridge on a graph moves mass between two distributions while charging a cost for the states visited. Previous work has approached this by learning the rates of a controlled continuous-time Markov chain...
Decoding Looped Transformers Better for (Almost) Free
Looped Transformers achieve parameter efficiency by repeatedly executing a shared block across recurrent loops. Each loop yields an intermediate representation decodable for the same next token, yet standard decoding discards earl...
SoftServe: A Scalable Quasi-Newton Method for Deep Learning
Quasi-Newton methods have long been among the most effective approaches for large-scale unconstrained convex optimization, yet two obstacles have limited their adoption in deep learning: non-convexity and enormous parameter sizes....
From Gradients to Capabilities: Understanding Multi-Teacher On-Policy Distillation
Multi-teacher on-policy distillation aims to combine the strengths of RL-trained teachers in a single student, but how teacher signals affect parameter changes remains underexplored. Researchers studied Qwen3-1.7B with four domain...
Muon meets Tamed Langevin: Momentum Preconditioning beyond Convex and gradient-Lipschitz Potentials
The Muon optimiser has become a workhorse for large language model pretraining, delivering state-of-the-art efficiency gains over AdamW. Its defining trick is orthogonalising matrix-valued momentum, replacing every nonzero singula...
Faynt: Scaling and Optimizing Policies for Competitive Melee
Competitive real-time strategy and fighting game AI have seen rapid progress, but Super Smash Bros. Melee remains a particularly challenging benchmark. Its 52-character roster, frame-perfect inputs, and continuous state space dema...
Are We Recovering Mechanisms? Objective-Level Recovery Gaps in Mechanistic Interpretability
Mechanistic interpretability seeks to recover the internal computations that produce model behavior. Circuit analysis represents a computation as a sparse subgraph of interacting model components. Detailed studies have reconstruct...
Semifactual Credit-Augmented Policy Optimization
Reinforcement learning with verifiable rewards improved reasoning capabilities of large language models yet predictions stayed sensitive to task-irrelevant prompt features. Researchers investigated this sensitivity through semifac...