llm

The article argues that AI, particularly large language models, lack intent and motivation, which undermines concerns about AI posing threats or going rogue.

A concept for a privacy middleware that anonymizes sensitive data before sending to LLMs to protect privacy, with local placeholder mapping, seeking feedback on feasibility.

This paper proposes a quantization-robust unlearning framework for large language models, using loss landscape analysis to ensure effective forgetting while maintaining model utility after compression.

SR-Fraud is an outcome-supervised reflective LLM agent framework for non-stationary payment fraud detection, improving detection metrics over traditional methods on a production benchmark.

The paper proposes LLMAE, a method to repurpose pre-trained decoder-only LLMs as continuous text autoencoders using a latent bottleneck, achieving high-fidelity reconstruction and enabling downstream tasks like image captioning.

COPE is a novel optimization framework for continual personalization of large language models under sparse user feedback, using learnable user embeddings and self-evaluation calibration. Experiments show it outperforms training-free and training-based baselines and remains robust in various settings.

This paper introduces NAF-Bench to study how large language models adhere to specified negation semantics, finding that frontier models like o4-mini perform well while open-source models lag, and suggesting improvements via solver delegation or fine-tuning.

EviStreams is an open-source, no-code web platform that uses human-in-the-loop AI for data extraction in systematic reviews, demonstrating that extraction quality is more dependent on field specification than model choice.

This paper introduces MORSE, a compression-aware method for evidence-preserving context ordering in large language models that improves evidence retention and downstream QA performance.

This paper introduces Planned Test-Time Scaling (PTTS), a method that coordinates reasoning branches to enhance performance on challenging tasks, achieving significant gains over repeated sampling in mathematical reasoning benchmarks.

The paper investigates fine-tuning strategies for customer support LLMs, comparing multi-task training, sequential updates, and model merging across multiple model families. It concludes that multi-task full fine-tuning is the most robust default, while specialist models degrade off-task and require reliable routing.

This paper introduces an approach for automatically generating generalized plans in Lean with formal proofs of completeness using LLMs, evaluated on benchmark domains showing significant advancements in automatic plan verification.

LEGO is a dual-module framework that synergizes Expert GraphRAG and Expert Chain-of-Thought to enhance complex legal reasoning in large language models, achieving superior performance on benchmarks like LawExamQA_Civil.

The Mercury 2.5 LLM achieves a speed of 770 tokens per second, as evaluated by Artificial Analysis through various intelligence benchmarks and capability indexes.

Paul Graham advises a CS undergrad that in the AI age, one can have the most impact by either developing LLMs or using AI to directly serve customer needs.

Redis LangCache is a semantic caching tool that reduces LLM costs by up to 70% by storing and reusing similar question-response pairs, making AI applications faster and more cost-effective.

The article argues that LLMs do not need to become ASI themselves; they can research and develop new AI models that might not be LLMs, leading to ASI through narrow super-intelligence.

This paper provides a mechanistic comparison of knowledge-conflict circuits in LLMs under instruction tuning, finding that tuning gates rather than rewires these circuits across multiple model families, with implications for interpretability.

The study reveals that chat templates control whether language models adopt a disclaimer voice (e.g., 'I'm just an AI') or an experiential voice (e.g., 'I feel'), and identifies an activation direction that can steer this behavior, impacting AI safety research.

This research presents a Structured Knowledge Tree architecture to control narrative reliability and epistemic pacing in LLM-driven detective games, reducing hallucinations by 64.78% and preventing premature information disclosure.