Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.
arXiv:2504.18225v2 Announce Type: replace Abstract: We introduce a new generation of small reasoning models for RAG, search, and source summarization. Pleias-RAG-350m and Pleias-RAG-1B are mid-traine...
arXiv:2610.07545v1 Announce Type: new Abstract: We present a fully offline speech-to-speech translation pipeline that runs on a Jetson Nano (4 GB) and corrects its own weak translations without retra...
arXiv:2610.07447v1 Announce Type: new Abstract: Autoresearch agents tackle open-ended problems by repeatedly proposing candidate solutions, evaluating them, and using feedback to guide subsequent exp...
arXiv:2610.07269v1 Announce Type: cross Abstract: Cross-view geo-localization is commonly solved as an image retrieval problem, matching a ground-level image against a database of satellite tiles thr...
arXiv:2610.07489v1 Announce Type: cross Abstract: Increasing connectivity to the outside world and the lack of inbuilt security mechanisms have made legacy intra-vehicular networks vulnerable to cybe...
arXiv:2610.07572v1 Announce Type: cross Abstract: In-context learning (ICL) adapts frozen large multimodal models (LMMs) to new tasks from a few demonstrations (demos), but re-encodes them at every q...
arXiv:2610.08463v1 Announce Type: cross Abstract: Long-context inference and Retrieval-Augmented Generation (RAG) handle evidence selection at vastly different scales, from a single long prompt to an...
arXiv:2602.02427v4 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved significant breakthroughs across various domains, but they can still produce unreliable or misleading ou...
arXiv:2610.06963v1 Announce Type: new Abstract: Rotary Position Embedding (RoPE) encodes token positions by rotating each two-dimensional channel of the query and key vectors at a channel-specific fr...
arXiv:2610.08303v1 Announce Type: new Abstract: Current evaluation of multilingual Large Language Models (LLMs) rests on an implicit Translation-Isomorphism Assumption (TIA): that semantic structures...
arXiv:2507.02115v3 Announce Type: cross Abstract: Synthesizing second-language (L2) speech is potentially highly valued for L2 language learning experience and feedback. However, due to the lack of L...
The paper introduces Hierarchical Continuous Diffusion Language Models (H-CDLMs), a framework that jointly diffuses multiple token modalities—individual tokens and coarser clusters of token embeddings—to enhance continuous diffusion language models. Applied to the CoBit architecture, the resulting H-CoBit achieves significant empirical gains, improving MAUVE scores and achieving lower generative perplexity on LM1B and OWT, while also outperforming prior continuous diffusion models on GSM8K. The approach generalizes to other continuous generative paradigms, as shown by consistent improvements when applied to the flow matching model FLM.
The paper investigates how diffusion models, even after fine‑tuning to unlearn harmful concepts, still retain a linear residual of those concepts in token embeddings. It introduces SubAttack, a jailbreak method that extracts this residual by learning orthogonal attack tokens composed of interpretable textual elements, and SubDefense, a plug‑and‑play defense that projects out the same subspace to suppress the residual. Experiments show SubAttack outperforms prior attacks in power and transferability, while SubDefense offers stronger robustness and better preservation of safe generation quality.
arXiv:2610.07008v1 Announce Type: new Abstract: Few-shot class-incremental learning (FSCIL) aims to learn novel classes from limited annotations while preserving prior knowledge. Existing methods typ...
arXiv:2610.07689v1 Announce Type: new Abstract: Vision-Language Models (VLMs) such as CLIP excel in global semantic alignment but often lack fine-grained perceptual capabilities. This hinders dense p...
arXiv:2606.03715v3 Announce Type: replace Abstract: Text-to-image models rely on text prompts as their primary interface to human intent. Prompts are encoded by a text encoder into embeddings that co...
The paper investigates how a small grounding model for a GUI agent should receive the action type. Using Qwen2‑VL‑2B fine‑tuned with LoRA on Android, the authors compare a flat baseline with five conditioning methods—auxiliary loss, hard‑routed action word, additive learned embedding, prepended learned token, and prompt word—under matched data, compute, and decoding settings. Results show that auxiliary loss, additive embedding, and prompt word each improve hit@0.10 by five to seven points over the baseline, while hard routing and prepended token perform similarly to the baseline; however, removing a problematic off‑screen touch point class lifts the baseline by nearly seven points, after which no method outperforms it. "whyItMatters":"The study identifies effective strategies for encoding action type in small grounding models, which could improve the accuracy of GUI agents in real‑world Android applications."
The paper presents a vision‑language navigation system that transfers from simulation to a real Ackermann‑steered mobile robot without relying on navigation graphs or panoramic views. It uses a Cross‑Modal Attention architecture trained on simulated data and fine‑tuned with limited real‑world episodes, leveraging linear photometric adjustments and a camera‑LiDAR sensor suite. Evaluation with SPL and nDTW metrics shows robust, adaptable navigation in continuous environments.
The paper investigates how large language model agents can over-rely on agentic memory, a phenomenon where retrieved memories distort inference even when they are correctly stored and retrieved. It shows that memory is helpful when past experience fully transfers to the current task but becomes misleading under partial query-memory overlap, a pattern confirmed by controlled experiments. To address this, the authors propose MEMTRIM, a plug‑and‑play framework that indexes memory evidence at write time and limits its reuse at read time, thereby reducing over-reliance without retraining and preserving useful memory benefits across models and memory architectures.
arXiv:2610.07570v1 Announce Type: new Abstract: In clinical practice, agreement among independent experts is treated as evidence of reliability, and multi-round consensus has become a core mechanism...