Dual-adapter routing system improves knowledge editing precision in LLMs
A new arXiv paper introduces a route-specialized dual-adapter architecture for knowledge editing in LLMs, separating the concerns of writing edits (edit adapter) and suppressing them when irrelevant (locality adapter). A relevance router gates which adapter is applied, addressing the locality problem in memory-assisted editing. Evaluated on CounterFact, zsRE, and MQuAKE benchmarks using Llama-3.1-8B-Instruct and Qwen3-8B, the method achieves best-in-class probability-preference accuracy across all three datasets. Ablations show the gain comes from the architectural separation rather than increased parameter capacity.
Related guides (2)
Related events (8)
Knowledge editing via locate-then-edit transferred to masked diffusion language models, revealing multi-token failure mode
A new arXiv paper investigates whether locate-then-edit knowledge editing methods, developed for autoregressive models, transfer to masked diffusion language models (MDMs) such as LLaDA and Dream. The authors find that causal tracing identifies the same early-to-mid-layer MLP location in both paradigms, but MDMs degrade systematically on multi-token edits due to partially unmasked intermediate states that the edit was never optimized for. A correction targeting these intermediate states substantially restores multi-token editing performance. The work is the first systematic comparison of knowledge editing across autoregressive and diffusion-based language model paradigms.
Adversarial Subspace Alignment for Robust Multimodal Knowledge Editing in MLLMs
This paper addresses the generalization gap in multimodal large language model (MLLM) knowledge editing, where edits fail to propagate across semantically equivalent visual and linguistic variations. The authors introduce Latent Adversarial Robustification (LAR), which generates adversarial but semantically coherent variants in joint latent space, and Rank-Constrained Subspace Learning (RCSL), which enforces low-rank alignment of adversarial representations at the edit layer. Together these form the ASAM framework, which formalizes robustness via knowledge units grouping semantically equivalent multimodal inputs. Empirical analysis demonstrates improved generality without sacrificing reliability or locality.
Adaptive LLM tutoring system with subject-aware prompt routing improves high-school student engagement
Researchers develop and evaluate an LLM-based tutoring system that uses a learned prompt routing model to dynamically select pedagogical strategies based on 14 features extracted from conversation transcripts. The system was trained in simulation and deployed in an A/B test with 359 high-school students (656 conversations), showing sim-to-real transfer and reducing required interactions by ~3 turns. A stochastic routing strategy achieved a notably higher exercise conversion rate (28.1%) compared to a greedy router (19.1%) and static baseline (19.6%).
ETCHR: Decoupled Image Editing for Visual Chain-of-Thought Reasoning in MLLMs
ETCHR introduces a question-conditioned, reasoning-aware image editing model that decouples visual transformation from downstream understanding in multimodal LLMs. It addresses two identified gaps—language-side (mapping abstract questions to visual edits) and generation-side (edit quality degrading with reasoning depth)—via a two-stage training recipe combining supervised fine-tuning on edit trajectories and VLM-derived reward signals. Because the editor is decoupled, it plugs into arbitrary MLLMs without retraining, yielding Pass@1 gains of roughly +4.6 to +5.5 points across five task families when paired with Qwen3-VL-8B, Gemini-3.1-Flash-Lite, and Kimi K2.5. The work advances the 'think with images' paradigm beyond fixed toolkits and unified multimodal approaches.
DIRECT: Adaptive test-time compute routing for embodied VLM planners
Researchers introduce DIRECT, a routing framework that dynamically allocates test-time compute for Vision-Language Models acting as embodied planners, using multimodal scene context to decide per-prompt how much compute to spend. Experiments on VLABench and RoboMME benchmarks show that different scaling axes (chain-of-thought depth, model size, memory history) yield qualitatively distinct gains, and that naive uniform scaling is wasteful. On a physical Franka arm, DIRECT matches or exceeds a stronger model's success rate at up to 65% lower average latency, improving the success-cost Pareto frontier.
Study compares human and LLM active causal reasoning, finding LLMs less efficient but near human-level on conjunctive rules
A new arXiv paper investigates whether active exploration reduces the 'conjunctive handicap' in causal learning, using a blicket detector task with adult participants who could freely intervene to identify causal objects. Results show active exploration substantially improves human conjunctive causal reasoning, though conjunctive rules still require more tests than disjunctive ones. State-of-the-art LLMs approach human-level hypothesis inference accuracy but show less efficient exploration strategies and similar conjunctive-disjunctive performance gaps, raising questions about the nature of LLM causal reasoning.
KVEraser: Learned KV cache editing for efficient localized context erasing in LLMs
KVEraser is a learned method for efficiently erasing specific spans from an LLM's KV cache without full recomputation of subsequent tokens. The approach replaces only the KV states of the erased interval with learned steering states, using a two-stage training pipeline of generic pre-training followed by task-specific fine-tuning. On contexts from 1K–32K tokens, KVEraser nearly matches full recomputation quality while incurring only 24% latency overhead versus a 17.6x increase for exact recomputation, with demonstrated generalization to long-document QA with harmful factual distractors.
Expert Tying reduces MoE LLM memory footprint by ~2x with minimal quality loss
Researchers introduce Expert Tying, an architectural modification for Mixture-of-Experts LLMs that shares expert parameters across consecutive transformer layers while keeping routing and attention layer-independent. Evaluated on OLMoE, Qwen3, and DeepSeek-style MoE architectures, the method achieves nearly 2x memory reduction with negligible perplexity or downstream quality degradation. The approach exploits parameter redundancy in MoE pathways to improve the compute-to-memory trade-off for training and inference.