LOGOS is a unified generative language model that represents scientific objects and their interactions as token sequences in a shared grammar. It achieves consistent or superior performance across diverse natural science tasks, demonstrating the feasibility of a single model serving multiple domains. The model scales positively with parameter count, and its design suggests that AI for Science should align deeply with large language models through shared architectures and training.
arxiv
arXiv cs.CL
·
54d ago
·
research
LOGOS: A General-Purpose Generative Model for Natural Sciences
from English
Importance 3/3
New feature vs. leaders
arXiv cs.CL
OpenAI
Google DeepMind
Mistral AI
AI agents
Open weights
Reasoning models
Related articles
arxiv
arXiv cs.AI
·
51d ago
LedgerAgent: Structured State for Policy-Adherent Tool-Calling Agents
LedgerAgent introduces a structured ledger to maintain task states separately in tool-calling agents. It renders states into prompts and enforces policy constraints before tool execution, reducing policy violations and improving performance across customer-service domains.
arxiv
arXiv cs.CL
·
51d ago
·
1 view
LedgerAgent: Structured State for Policy-Adherent Tool-Calling Agents
LedgerAgent introduces a structured ledger to maintain task states separately in tool-calling agents. It renders these states into prompts and enforces policy constraints before tool execution, reducing policy violations and improving performance across customer-service domains.
arxiv
arXiv cs.AI
·
51d ago
·
2 views
Dual-Agent Framework for Cross-Model Verified Translation
A dual-agent framework converts natural-language experiment protocols into executable commands for robotic lab platforms. It uses a Parser Agent and a rule-based mapping engine to translate protocols, with a heterogeneous LLM Validation Agent ensuring accuracy and triggering self-correction. The framework successfully enables end-to-end autonomous execution of microplate-based experiments like the Bradford assay.
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%.