A new preprint called TEPA treats memory validity as a first-class state, revoking outdated precedents when new evidence conflicts while keeping audit trails. It outperforms append-only and last-write-wins in a complete-reversal experiment, though results are not yet independently reproduced.
The authors propose Synthetic Query Probing, a simple yet effective approach to compare different embedding models by comparing similarity match scores across models instead of raw embedding spaces. The paper illustrates non-linear relationships between models like Ada and Titan, offering practical guidance for swapping embedding models and setting retrieval thresholds.
Multiverse Computing announces a paper on making LLM knowledge distillation cheaper via offline top-K logits and a fused chunked KL loss, cutting VRAM usage for distillation at scale.
A new neuromorphic AI framework inspired by cognitive science could complete tasks more efficiently than current approaches.
NBER working paper examining the long-run economic effects of H-1B immigration on the U.S. economy, released July 2026.
CoinRAG is a new method for long-context RAG that reuses fine-grained contextualized information nugget KV caches instead of full chunks, improving efficiency and answering quality. It achieves a new Pareto frontier with 5.3% relative F1 improvement on LongBench multi-hop QA tasks.
This exploratory paper evaluates LLM-assisted rewriting of moderate-complexity financial sentences for DisCoCat-based sentiment analysis, finding that prompt-based compression can reduce circuit complexity by over 70% and slightly improve accuracy compared to a low-complexity baseline.
Presents LitTraceQA, a benchmark for scientific question answering that requires systems to retrieve relevant papers, locate supporting evidence, and produce verified answers in multiple formats.
This paper introduces a concept-centric benchmark to probe LLMs' understanding of geo-spatial concepts like direction, distance, and topology, testing abstraction, compositionality, and grounding across various model architectures and scales. Findings reveal clear limitations in current LLMs' conceptual understanding.
This paper critiques existing benchmark contamination mitigation metrics and proposes SA-PPG (Stratified Aggregate of Per-question Probability Gaps) for more reliable evaluation, alongside RailCap, a decoding-time mitigation method that caps greedy fallback tokens to suppress memorization.
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 paper presents the development of Cantonese and Irish treebanks within the ParGram Project and investigates the potential and limitations of using multilingual LLMs (OpenAI's gpt-oss-120b) for grammar engineering tasks such as translation and syntactic structure generation.
This paper proposes using off-the-shelf CLIP-style multimodal encoders with a bimodal attribution method to predict gaze behavior in visual world experiments, successfully replicating a seminal study on human predictive processing without fine-tuning.
This paper investigates why language models fail at two-hop generalization, showing that models succeed when the second hop follows training distribution but fail when it deviates, and proposes a recurrent-style training strategy to improve out-of-distribution two-hop reasoning.
The paper introduces Stoicheia, a 405M-parameter character-level masked diffusion encoder for Ancient Greek that unifies textual restoration, parsing, and metrical scansion in a single model, outperforming prior systems like Ithaca on benchmark tasks.
This paper introduces the 'crystallization problem' for evaluating reusable memory in text-to-SQL systems, showing that storing verified corrected queries in a per-database bank improves held-out first-attempt accuracy by 4.34 points on BIRD, capturing 44.4% of the headroom provided by on-demand repair. Controlled interventions identify database-specific content as the main driver.
This paper proposes measuring concept content in text using LLM internal activations via linear probes and RFM concept vectors, applied to ESG classification. The best linear probe approaches fine-tuned classifier accuracy without task-specific fine-tuning and outperforms the model's own output, showing activations carry concept content beyond responses.
This paper presents HNR-DAC, a two-stage framework for scientific claim verification over cited papers, combining hard-negative reranking and distribution-aligned classification. It achieves strong results on NLPCC 2026 Task 10 Track 2, ranking third on the leaderboard with the highest Macro-F1.
This paper proposes a hybrid knowledge graph generation pipeline that combines top-down grounding in Wikidata with bottom-up agentic synthesis to handle noisy, multilingual HR skill declarations, producing a scalable and self-healing skills taxonomy.
The paper investigates whether retrieving more evidence helps visual retrieval-augmented generation with diffusion language models, finding that unconditionally expanding evidence hurts accuracy due to semantic conflict, and proposes a training-free Entropy-Based Candidate Filter (ECF) to selectively admit evidence, improving accuracy across benchmarks.