arXiv:2608.05164v1 Announce Type: new Abstract: Independently trained large language models may develop shared internal representations of semantic concepts despite architectural differences -- but whether this geometric…
arXiv:2608.02376v2 Announce Type: replace-cross Abstract: Search and database engines still store text as UTF-8, a format built for humans. But the systems that increasingly read and…
arXiv:2608.05163v1 Announce Type: new Abstract: A common assumption holds that switching to a non-English language makes a multilingual RAG system easier to attack for personal information.…
arXiv:2508.02091v4 Announce Type: replace-cross Abstract: Approximate nearest-neighbor search (ANNS) algorithms have become increasingly critical for recent AI applications, particularly in retrieval-augmented generation (RAG) and agent-based LLM…
arXiv:2608.05162v1 Announce Type: new Abstract: Pooling is a consequential but under-examined design choice in decoder-only concept representation work: practitioners must collapse token-level hidden states into a…
arXiv:2608.05153v1 Announce Type: new Abstract: GraphRAG underperforms vector RAG on citation precision in many reports, but where and why have remained corpus-bound. We present a triple-robustness…
arXiv:2608.05154v1 Announce Type: new Abstract: Rotary positional encoding (RoPE) is a core component of modern language models and has been extended to multimodal LLMs through multidimensional…
arXiv:2608.05155v1 Announce Type: new Abstract: Traditional sentiment analysis (SA) models, while effective for polarity classification, provide limited insight into the rhetorical, ideological, and framing dimensions of…
arXiv:2608.05157v1 Announce Type: new Abstract: Double blind peer review serves as the scientific community primary defense against status and affiliation bias. Its effectiveness rests on the…
arXiv:2608.05156v1 Announce Type: new Abstract: Post-training of large language models optimizes only parameters, while inference-time procedural scaffolds are typically designed independently of parameter training. This disconnect…
arXiv:2608.05161v1 Announce Type: new Abstract: Instruction-tuned LLMs are deployed into environments where domains evolve, yet extending a fine-tuned model's capabilities without full retraining remains an unsolved…
arXiv:2608.05158v1 Announce Type: new Abstract: In biological evolution, unconstrained mutation can lead to catastrophic outcomes: organisms may evolve enhanced capabilities while losing essential functions for survival.…
arXiv:2608.05876v1 Announce Type: cross Abstract: User requests serve as research specifications for deep research agents, shaping what evidence to seek and how to synthesize it. In…
arXiv:2604.22191v2 Announce Type: replace-cross Abstract: In agentic workflows, LLMs frequently process retrieved contexts that are legally protected from further training. However, auditors currently lack a reliable…
arXiv:2502.14671v4 Announce Type: replace Abstract: Large Language Model (LLM) representations are known to align with brain activity during language processing, but it remains unclear what drives…
arXiv:2608.06167v1 Announce Type: cross Abstract: We present a schema-based framework for extracting complex, structured information from unstructured text documents using generative AI, followed by automated semantic…
arXiv:2605.26352v3 Announce Type: replace Abstract: Retrieval is increasingly moving from one-shot matching toward interactive reasoning, where language agents iteratively inspect evidence, reformulate queries, and search again.…
arXiv:2511.17241v2 Announce Type: replace Abstract: Understanding and predicting user behavior on social media platforms is crucial for content recommendation and platform design. While existing approaches focus…
arXiv:2608.05233v1 Announce Type: new Abstract: Dreams can be emotionally intense but difficult to communicate. We describe the Dream Scene Visualiser (DSV) system which turns written dream…
arXiv:2608.06364v1 Announce Type: cross Abstract: The use of e-commerce mobile applications is expanding in Nigeria, creating both opportunities and risks, including fraud and reduced user control…
arXiv:2608.05228v1 Announce Type: new Abstract: The "decompose-then-verify" paradigm for LLM factuality evaluation faces a fundamental trade-off: atomic facts, i.e., one sentence conveying one unit of information,…
arXiv:2608.06153v1 Announce Type: cross Abstract: Automated skill evolution enables Large Language Model (LLM) agents to continuously improve without expensive retraining. However, existing approaches typically treat skill…
arXiv:2608.05225v1 Announce Type: new Abstract: Research agents can increasingly search literature, propose hypotheses, generate code, run experiments, and draft manuscripts from a single topic. However, a…
arXiv:2608.05877v1 Announce Type: cross Abstract: Optimal transport (OT) has emerged as an effective framework for unsupervised action segmentation. Yet, in existing OT-based methods, the latent action…