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lgalke 

authored a paper 28 days ago

The Arbiter Agent: Continually Monitoring Multi-Agent Conversations to Detect Emergent Misalignment

Paper • 2606.10747 • Published Jun 9 • 13

lgalke 

authored 11 papers about 1 month ago

BrainSurgery: Reproducible and Reliable Declarative Weight Manipulations for Model Editing and Upcycling

Paper • 2606.09707 • Published Jun 8 • 8

DeToNATION: Decoupled Torch Network-Aware Training on Interlinked Online Nodes

Paper • 2502.06728 • Published Feb 10, 2025

Are We Really Making Much Progress in Text Classification? A Comparative Review

Paper • 2204.03954 • Published Apr 8, 2022

Efficient Continual Learning for Small Language Models with a Discrete Key-Value Bottleneck

Paper • 2412.08528 • Published Dec 11, 2024

FlexMoRE: A Flexible Mixture of Rank-heterogeneous Experts for Efficient Federatedly-trained Large Language Models

Paper • 2602.08818 • Published Feb 9 • 3

DaLA: Danish Linguistic Acceptability Evaluation Guided by Real World Errors

Paper • 2512.04799 • Published Dec 4, 2025

SommBench: Assessing Sommelier Expertise of Language Models

Paper • 2603.12117 • Published Mar 12 • 1

Training Language Models to Use Prolog as a Tool

Paper • 2512.07407 • Published Apr 19

The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment

Paper • 2605.07462 • Published May 8 • 3

Emergent Languages in Populations of Language Model Agents: From Token Efficiency to Oversight Evasion

Paper • 2605.31170 • Published May 29 • 12

LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs

Paper • 2606.06286 • Published Jun 4 • 8

lgalke 

authored a paper about 2 months ago

Confidence and Calibration of Activation Oracles for Reliable Interpretation of Language Model Internals

Paper • 2605.26045 • Published May 25 • 12

lgalke 

authored a paper 9 months ago

Guarded Query Routing for Large Language Models

Paper • 2505.14524 • Published May 20, 2025 • 2

lgalke 

authored 6 papers 11 months ago

Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models?

Paper • 2502.11895 • Published Feb 17, 2025 • 3

What makes a language easy to deep-learn? Deep neural networks and humans similarly benefit from compositional structure

Paper • 2302.12239 • Published Feb 23, 2023 • 1

Dynaword: From One-shot to Continuously Developed Datasets

Paper • 2508.02271 • Published Aug 4, 2025 • 15

GenCodeSearchNet: A Benchmark Test Suite for Evaluating Generalization in Programming Language Understanding

Paper • 2311.09707 • Published Nov 16, 2023

When are 1.58 bits enough? A Bottom-up Exploration of BitNet Quantization

Paper • 2411.05882 • Published Nov 8, 2024 • 1

CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model

Paper • 1902.06423 • Published Feb 18, 2019