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30 result(s) for “okapi bm25 site hotchips pp site github com site via do unit it site huggingface co site:arxiv.org” of 455 matched · 1555 ms
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Freshness: newest result fetched 24 d ago · median age of these results 24 d · 1 of 30 carry a fetch time
The query asks the model to recall the topic of an earlier conversation about Tesla; the gold answer is anchored in one specific session about Pooja’s dig site
from the top result below
https://arxiv.org/html/2608.02613v1
With the interaction history, it co-generates ground truth over six recall, reasoning, and trustworthiness evaluation dimensions
https://arxiv.org/list/cs.AI/new
Extending the pattern-generation paradigm of Neural Cellular Automata (NCA), it navigates the degenerate Boolean search space to satisfy a computational task, rather than regenerating a fixed target state
https://arxiv.org/html/2510.03215v2
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arXiv is now an independent nonprofit
https://arxiv.org/html/2407.16741v3
edu
Abstract
Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways
https://arxiv.org/html/2608.03063v1
Compared with the production DeepSeek-based LLM baseline, it improves screening precision from 92
https://arxiv.org/list/cs.CL/new
With the interaction history, it co-generates ground truth over six recall, reasoning, and trustworthiness evaluation dimensions
https://arxiv.org/html/2509.22186v2
com/opendatalab/MinerU \metadata[Model:]https://huggingface
https://arxiv.org/list/math/new
CO)
Let $X=\{x_1,\dots,x_n\}\subset \mathbb{R}^2$ be a finite set of points of diameter at most $1$
https://arxiv.org/abs/2608.03883
Our systems combine sparse BM25 retrieval, evidence-aware rescoring for self-referential symptom reports, embedding-based reranking, query-prototype expansion, and LLM-based reranking
https://arxiv.org/abs/2608.03860
The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation
https://arxiv.org/abs/2104.08663
Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs
https://arxiv.org/html/2501.13956v1
com
Abstract
We introduce Zep, a novel memory layer service for AI agents that outperforms the current state-of-the-art system, MemGPT, in the Deep Memory Retrieval (DMR) benchmark
https://arxiv.org/html/2608.03048v1
6 million tokens and find that it outperforms the recurrent-memory baseline by +6
https://arxiv.org/html/2608.02616v1
com
Abstract
We present the first independent, systematic evaluation of OpenAI’s Privacy Filter (OPF), a 1
https://arxiv.org/html/2608.02975v1
com
Abstract
Large language models (LLMs) have demonstrated impressive performance in MQM-based translation quality (TQ) evaluation, and recent advances in large reasoning models (LRMs) promise even greater improvements
https://arxiv.org/html/2607.26245v1
We do not claim trading profitability—the central empirical finding is the opposite, and that negative result is published with the data that produced it
https://arxiv.org/html/2608.03655v1
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We formulate sentence-level content planning as a combinatorial selection problem that combines relevance, factuality, and redundancy, and instantiate it with a DPP-inspired determinant-based quality–diversity objective
https://arxiv.org/html/2508.16279v1
Following this trend, the focus of LLM-based agent frameworks has shifted from relying solely on intrinsic reasoning to empowering agents to perceive and interact with environments via an array of tools
https://arxiv.org/html/2407.02518v2
com
Abstract
Large language models (LLMs) for code are typically trained to align with natural language instructions to closely follow their intentions and requirements
https://arxiv.org/html/2407.04503v4
2 To what extent do multi-turn transmissions affect the evolution of properties
https://arxiv.org/html/2506.01844v1
2 Related work
Vision-language models (VLMs).
Vision-language-action models (VLAs).
3 SmolVLA: small, efficient and capable
Overview.
3.1 Model architecture
Vision-language model (VLM).
State, action, and feature projectors.
Visual
https://arxiv.org/abs/1910.03771v4
03771v4] HuggingFace's Transformers: State-of-the-art Natural Language Processing
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arXiv is now an independent nonprofit
https://arxiv.org/abs/1910.03771v5
03771v5] HuggingFace's Transformers: State-of-the-art Natural Language Processing
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arXiv is now an independent nonprofit
https://arxiv.org/abs/1910.03771
03771] HuggingFace's Transformers: State-of-the-art Natural Language Processing
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arXiv is now an independent nonprofit
https://arxiv.org/abs/2606.07379
Detecting and Preventing Cheating via Capped Evaluation with Randomized Tests
Authors:Thanawat Lodkaew, Johannes Ackermann, Soichiro Nishimori, Nontawat Charoenphakdee, Masashi Sugiyama, Takashi Ishida
View a PDF of the paper titled Do Coding Agents Deceive Us
https://arxiv.org/abs/1911.06462
In particular, we present an efficient algorithm, GMCOM, which guarantees that the average computational cost to the good ID per unit time is O(J + sqrt(T(J+1))), where J is the average number of joins by the good IDs and T is the average computational spending of the adversary
https://arxiv.org/abs/1911.06462v1
In particular, we present an efficient algorithm, GMCOM, which guarantees that the average computational cost to the good ID per unit time is O(J + sqrt(T(J+1))), where J is the average number of joins by the good IDs and T is the average computational spending of the adversary
https://arxiv.org/abs/1202.4083
4083] Search for neutral Higgs bosons decaying to tau pairs in pp collisions at sqrt(s) = 7 TeV
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https://arxiv.org/abs/astro-ph/0402288
Langer
View a PDF of the paper titled Helium accreting CO white dwarfs with rotation: helium novae instead of double detonation, by S
https://arxiv.org/abs/2006.03249
In the semi-synchronous model (SSYNC), the robots share a common notion of time; at each time unit, a subset of the robots is activated, and each performs all three actions (sensing, computation, and movement) in that time unit