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This paper introduces NLP Psychometrics, a framework that treats psychological prediction from text as a psychometric problem. Using LLM personas, emotional profiles, and syntactic-semantic networks with random forest regressors, it explains up to 76% of variance in mental health scores and shows promise and limits of synthetic data for psychometric prediction.
This preprint evaluates how six large language models respond to prompt framing and biased prompts across 160 prompts, finding that LLMs systematically adapt their responses to align with prompt framing even in factual contexts, potentially reinforcing user biases.
FutureBridge introduces a token reranker for collaborative decoding that ranks LLM-SLM candidates based on how well the SLM can continue reasoning from them, improving the Qwen3-1.7B SLM's math accuracy by 35.1% over greedy decoding.
A research paper introducing a multi-stage forward-query method to cryptanalytically extract isolated bias-free GLU feed-forward block weights, demonstrating sub-percent recovery accuracy on Qwen, Llama, and Gemma components while noting end-to-end model API attacks remain unsolved.
ReQuant introduces a backpropagation-free, fixed-grid discrete refinement stage for post-training quantization (PTQ) that iteratively improves initial quantized models while preserving the quantized format, showing consistent gains across various LLMs and bit-widths.
Gated-BEPO is a new credit assignment method for LLM agents that derives step-level credit from empirical rollout graphs using Bellman fixed-point estimation and adaptively fuses it with episode-level credit via a confidence gate. Experiments on WebShop, ALFWorld, and visual Sokoban show consistent improvements over existing critic-free methods.
Introduces AgentPatch, a training-free coarse-to-fine repair framework for merging agentic multimodal large language models, addressing asymmetric capability preservation and behavior-critical forgetting.
A discussion of how data poisoning and RAG manipulation pose a silent, dangerous threat to AI systems, arguing that security must extend beyond input filtering to memory, data pipelines, and multi-agent logic.
Promotional post about Horizon, an AI-driven trading strategy platform that lets users backtest and deploy strategies in plain English, citing a professor managing $200M with AI agents and returning 56% last year.
This paper surveys clinical communication processing using LLM-generated synthetic data and presents 13 case studies across EMS reports, nurse handoffs, and more, showing that synthetic data can bootstrap clinical NLP systems.
Introduces GROM, a gradient-free one-shot machine unlearning method that computes a closed-form additive weight update via ridge-regularized least squares, achieving state-of-the-art forgetting-utility trade-offs on benchmarks like TOFU and WMDP, and resisting quantization-based recovery attacks.
This paper investigates whether five open-weight LLMs exhibit human-like sensitivity to psycholinguistic factors in anaphor resolution, using surprisal and comprehension accuracy as behavioral measures. Results show selective cognitive alignment, with some models matching human discourse sensitivity but not semantic interference effects.
This paper investigates using retrieved document-simplification examples to guide LLM prompting for document-level text simplification, showing improvements over prompt-only generation on the OneStopEnglish corpus.
This paper proposes Circuit-Anchored Evolution (CAE), a method that uses mechanistic interpretability to identify and anchor a tiny safety circuit in LLMs during self-evolution, preventing models from misevolving into capable but dangerous systems while preserving capability.
This paper proposes DiSR, a framework that separates 3D perception from reasoning by using off-the-shelf perception models to reconstruct explicit 3D evidence and fine-tuning an LLM with LoRA for spatial reasoning, achieving competitive performance with improved interpretability and efficiency.
This paper investigates using GPT-3.5 for counterfactual analysis in online lending, showing that prompt engineering improves prediction accuracy and enables coherent counterfactual ROI generation under alternative interest rates.
OpenAI released AI-generated math breakthroughs that experts are calling research misconduct due to lack of academic rigor.
Presents RESPClinBench, a real-world scenario benchmark for respiratory clinical decision-making, evaluating seven LLMs on COPD and pulmonary nodule cases. Finds task-specific limitations including imaging hallucination and medication-safety risks.
A PhD proposal outlining a unified end-to-end framework for multilingual metaphor processing, integrating metaphor detection, translation evaluation, and joint modeling using linguistic theory and large language models.
The paper presents TourSynbio-Search, an LLM-driven agent framework for unified protein engineering search across literature and biological databases, powered by the TourSynbio-7B multimodal model with dual PaperSearch and ProteinSearch components.