arxiv arXiv cs.LG · 52d ago · research

Unsupervised Reward Optimization for Protein Language Models

from English

A new framework enables protein language models to generate controllable protein sequences without labeled data or wet-lab validation. It uses task-agnostic rewards based on model uncertainty and semantic consistency to guide generation, with Soft and Binarized Reward Optimization outperforming baselines in coverage and controllability across diverse conditions.

Importance 3/3 New feature vs. leaders New harness with differentiators arXiv cs.LG OpenAI Google DeepMind Mistral AI Code generation Evaluation & benchmarks Reasoning models

Read original

arxiv arXiv cs.LG · 51d ago

LLM-Generated GPU Kernels Face Correctness Illusion

Benchmarks using fixed-shape checks miss real bugs in LLM-generated GPU kernels. A controlled corpus of 24 kernels, including 9 buggy variants with transcription errors, reveals that an op-schema-aware oracle detects all failures and passes all correct controls, with identical results across five GPU architectures.

arxiv arXiv cs.CL · 51d ago · 2 views

AtomMem: Simple and Effective Memory System for LLM Agents

AtomMem introduces a memory system that stores high-value atomic facts from long-form interactions. It uses hierarchical event structures and temporal profiles to capture coherent episodic contexts and track evolving user attributes, enabling stable and efficient memory evolution. Experiments on the LoCoMo benchmark show AtomMem achieves state-of-the-art performance in reasoning tasks.

arxiv arXiv cs.CL · 51d ago

GEMS: Geometric Constraints Enable Multi-Semantic Superposition in LLMs

GEMS enables training-free superposition of multiple semantic directions in LLMs by addressing distributional deviation and directional interference through geometric constraints. On GSM8K, it maintains 98% accuracy with three non-mathematical directions, while unconstrained addition drops to 4%; on Wikitext-2, it increases PPL by only 2.2%.

arxiv arXiv cs.LG · 52d ago · 2 views

Discriminator-Guided RL Corrects Flow Matching with Data-Aligned Rewards

Discriminator-Guided RL (DRL) uses a pretrained representation space to train a discriminator that separates real data from model-generated samples. Its logit is used as a reward in KL-regularized RL, aligning model outputs with visual and semantic realism without human preferences. DRL improves FID and semantic FD across models like SiT and JiT, and enhances the Pareto frontier between preference and fidelity.

arxiv arXiv cs.AI · 52d ago

LLM-as-Interface, ML-as-Predictor for Pediatric Appendicitis

ClaMPAPP, a hybrid system, uses an LLM to extract structured clinical features from free-text notes and passes them to an XGBoost classifier for diagnosis. It outperformed end-to-end LLMs in both internal and external validation, with better diagnostic performance and fewer missed cases, demonstrating superior stability and safety in pediatric appendicitis triage.