Llm + Reasoning | hypedar

Llm + Reasoning

23.0

Develop an automated interpretability tool that generates human-readable descriptions of internal model circuits. This would democratize research into how models arrive at specific conclusions.

+0

emergingimplementation gap

reasoningunsupervisedgeospatialoptimizationbenchmarkingllmembeddingsclustering

Signals (20)

SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs

TEMPER: Testing Emotional Perturbation in Quantitative Reasoning

ADAG: Automatically Describing Attribution Graphs

Execution-Verified Reinforcement Learning for Optimization Modeling

Reasoning-Based Refinement of Unsupervised Text Clusters with LLMs

Introducing the Child Safety Blueprint

RAGEN-2: Reasoning Collapse in Agentic RL

From Broad Exploration to Stable Synthesis: Entropy-Guided Optimization for Autoregressive Image Generation

Riemann-Bench: A Benchmark for Moonshot Mathematics

Evaluating the ethics of autonomous systems

MSA-Thinker: Discrimination-Calibration Reasoning with Hint-Guided Reinforcement Learning for Multimodal Sentiment Analysis

ProofSketcher: Hybrid LLM + Lightweight Proof Checker for Reliable Math/Logic Reasoning

Beyond Social Pressure: Benchmarking Epistemic Attack in Large Language Models

BioAlchemy: Distilling Biological Literature into Reasoning-Ready Reinforcement Learning Training Data

Google AI21h ago

ConvApparel: Measuring and bridging the realism gap in user simulators

Operational Noncommutativity in Sequential Metacognitive Judgments

Limits of Difficulty Scaling: Hard Samples Yield Diminishing Returns in GRPO-Tuned SLMs

Uncertainty-Guided Latent Diagnostic Trajectory Learning for Sequential Clinical Diagnosis

Enabling Intrinsic Reasoning over Dense Geospatial Embeddings with DFR-Gemma

Blind Refusal: Language Models Refuse to Help Users Evade Unjust, Absurd, and Illegitimate Rules