arxiv arXiv cs.CL · 53d ago · research

MambaCount: Efficient Text-guided Object Counting

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MambaCount introduces a spatial sparse state space duality block to enable efficient text-guided open-vocabulary object counting. It addresses causal modeling limitations and high entropy in spatial token responses, achieving state-of-the-art results on FSC-147 with a test MAE of 12.23 while maintaining linear complexity.

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

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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.LG · 51d ago

De-biased VLM-as-3D-Judge Protocol for Furniture Generation

A de-biased VLM-based judge protocol specializes TRELLIS on furniture generation using lightweight adaptation. The protocol addresses failure modes like image overload and geometry-hiding, with calibration showing 0.83–1.0 win rates and base-vs-base symmetry at 0.5. Among six adaptation methods, conditioner repair under severe degradation achieves parity with the base model, while no method exceeds a 65% win-rate target.

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.