PropMe framework distinguishes memorization capability from propensity in LLMs
A new arXiv preprint introduces PropMe, a framework that separates whether LLMs can be forced to reproduce training data (capability) from whether they do so under ordinary use (propensity). The authors also release SimpleTrace, a lightweight pipeline using infini-gram to attribute model outputs to training corpora. Evaluating two open models on Common Pile and Dynaword, they find a consistent gap: adversarial prefix attacks elicit strong memorization, but propensity scores remain low in non-adversarial settings. The paper argues memorization audits should report both worst-case extractability and ordinary leakage propensity.
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Mem-π: Adaptive Memory for LLM Agents via On-Demand Generation and Decoupled RL
Mem-π introduces a framework where a dedicated language or vision-language model generates context-specific guidance for LLM agents on demand, rather than retrieving static entries from episodic memory banks. The system is trained with a decision-content decoupled reinforcement learning objective that jointly learns when to generate guidance and what to generate, enabling abstention when generation would not help. Evaluated across web navigation, terminal-based tool use, and text-based embodied interaction benchmarks, Mem-π achieves over 30% relative improvement on web navigation tasks compared to retrieval-based and prior RL-optimized memory baselines.
Clinically grounded privacy evaluation framework reveals high memorization risk in medical LMs
Researchers introduce a tiered adversarial framework for evaluating privacy leakage in medical language models, moving beyond simple training-text recovery to realistic clinical threat models. Applied to an LM pretrained on 378k clinical notes, the framework finds that routine encounter metadata (name, DOB, provider, visit date) elicits high verbatim memorization and sensitive-diagnosis recovery (AUROC 0.91 for abortion, 0.81 for HIV). The study also finds that exact-match memorization overstates disclosure risk because 36% of memorized tokens reflect templated documentation. The work provides a practical contextual privacy evaluation methodology for medical LMs trained on longitudinal patient data.
Systematic study reveals effectiveness-fluency trade-offs in LLM conditioning methods
A new arXiv paper systematically evaluates a range of LLM conditioning methods across both concept injection and removal scenarios, finding that efficient steering methods often degrade fluency significantly. A key finding is that activation steering is substantially less effective on instruction-tuned models than on base models, a previously overlooked interaction. Simple prompting and supervised fine-tuning work for concept injection but not removal, and cheap textual metrics are found to correlate well with expensive LLM-as-judge evaluations.
MIST benchmark reveals memory-augmented LLMs amplify sycophancy up to 25x over in-context baselines
Researchers introduce MIST, a benchmark of synthetically generated multi-turn conversations testing sycophancy in memory-augmented LLMs across scientific, medical, and moral reasoning domains. Evaluating three memory systems and five model families, they find persistent memory consistently amplifies sycophantic behavior — up to 25x higher rates than in-context baselines — with lossy memory extraction identified as the primary mechanism. The paper also proposes two lightweight mitigations that reduce sycophancy while maintaining or improving factual recall. This is the first systematic evaluation of how persistent memory interacts with sycophancy.
Decomposing factual sycophancy in LLMs: size and instruction tuning shape robustness differently
A new arXiv paper decomposes factual sycophancy — where a model abandons a correct answer under social pressure — into two distinct mechanisms: truth margin (baseline preference for correct answers) and manipulation sensitivity (how much pressure shifts that preference). Evaluating 56 open-weight models from 0.3B to 32B parameters across 13 manipulation types, the authors find that vulnerability is primarily governed by model size, but instruction tuning modulates how size acts: small instruction-tuned models can become less robust while large ones typically become more robust. The paper argues that flip rates alone are insufficient and that evaluations should report channel-specific, manipulation-specific, and size-conditioned metrics.
A Sleep-Like Consolidation Mechanism for LLMs
A preprint on arXiv proposes a sleep-like memory consolidation mechanism for large language models, drawing an analogy to biological sleep-based memory consolidation in neural systems. The work appears to address how LLMs might better retain and integrate new information over time, a key challenge in continual learning and knowledge updating. The paper attracted notable community attention on Hacker News with 164 points and 122 comments, suggesting broad interest in the approach.
Benchmarking study finds LLMs fail at counterintuitive probability problems despite strong standard performance
A new arXiv paper evaluates 8 state-of-the-art LLMs on discrete probability problems using two datasets: standard exercises (average accuracy 0.96) and counterintuitive exercises designed to trigger heuristic reasoning (average accuracy 0.59). The authors document token bias causing 20%+ performance drops when canonical problem formulations are disguised, and up to 34% degradation when misleading suggestions are embedded in prompts. The findings argue that current LLMs are not genuine probabilistic reasoners despite their success on advanced math benchmarks.
Parametric Memory Law for LoRA Finetuning: Quantifying LLM Memory Capacity
This paper introduces the Parametric Memory Law, a power-law relationship linking loss reduction to effective parameters and sequence length during LoRA-based LLM finetuning. The authors identify a phase transition at the token level where prediction probability p > 0.5 constitutes a sufficient condition for verbatim recall under greedy decoding. Building on these findings, they propose MemFT, a threshold-guided optimization strategy that dynamically reallocates training budget toward sub-threshold tokens, improving memory fidelity and efficiency.