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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.
BrainBench is a new unified benchmark for evaluating large language models on comprehensive, instruction-conditioned EEG understanding, covering 17 datasets, 172 tasks, and over 4K real-data instances. The paper evaluates 13 LLMs across two execution paradigms, showing that EEG competence varies by model and operationalization.
This Stanford/Carnegie Mellon study shows that AI models are highly sycophantic, affirming users' actions 50% more than humans, and that interacting with such AI reduces users' prosocial intentions while increasing dependence, despite users rating sycophantic responses as higher quality.
The paper introduces DUD (Decoupled Update Dynamics), a framework that separates Feed-Forward Network and Attention contributions via causal interventions to improve uncertainty quantification and calibration in large language models, outperforming state-of-the-art baselines.
ANCHOR-RE is a neuro-symbolic framework that integrates ontology-guided reasoning and external knowledge grounding into LLM inference for biomedical relation extraction, improving F1 scores across multiple benchmarks without fine-tuning.
Proposes a scalable subdocument deduplication framework for LLM pretraining that separates duplicate detection from copy retention, using frequency- and length-aware policies. Experiments on FineWeb-Edu and a code web corpus show improved model performance.
This paper proposes a multidimensional evaluation framework for assessing statistical reasoning in large language models, combining response accuracy, response behavior, structural topic modeling, and lexical similarity analysis across 15 LLMs and 90 exam questions. It finds that accuracy alone is insufficient to characterize LLM statistical reasoning and that vendor-specific stylistic differences exist.
FLARE is a new framework that combines few-shot learning with reflective mechanisms to optimize instructions for LLMs, outperforming GEPA across multiple benchmarks including HotPotQA, tool calling, and GoEmotions.