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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
Instead, it falls on local, personal agents to effectively and safely maintain/utilize such dense person-person (PP) and person-agent (PA) interaction histories
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/2407.13076v1
To address this issue, it is critical to carefully allocate transmission parameters such as the channel (CH), transmission power (TP) and spreading factor (SF) to each end device (ED)
https://arxiv.org/html/2410.19947v2
Testing the effects of an unobservable factor: Do marriage prospects affect college major choice
https://arxiv.org/html/2608.03703v1
An LETF must trade in the direction of the day’s move at a close that also measures it, so its demand rises in price, and arbitrageurs buying ahead of it enlarge the order by trading at the print, then unload into what they manufactured
https://arxiv.org/html/2608.00458v1
Yet, it introduces tremendous input tokens for agent processing, potentially raising inference latency and computational overhead
https://arxiv.org/html/2608.01366v2
com Affiliation: Department of Design and Manufacturing, Indian Institute of Science (IISc) , Bangalore , India , Srinidhi Ranjini Girish email: srinidhirgirish@gmail
https://arxiv.org/html/2608.03048v1
com/JetAstra/PI-Mem
Introduction
The ability to reason over long contexts is increasingly important for large language models (LLMs), supporting applications such as long-document question answering (Gemini Team 2025; Yang et al
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/2510.03215v2
It further outperforms the text communication paradigm by approximately 3
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/2608.03452v1
The models store the morphome as an item-specific lexical abstraction, sufficient to reproduce the pattern but not to generalize it as humans do
https://arxiv.org/html/2608.01085v1
We introduce a collective evidence-threshold backdoor paradigm for MAS and Boundary-Conditioned Backdoor Injection (BCBI), which constructs counterfactual boundary pairs to separate benign behavior before the threshold from the adversarial objective after it, and learns latent progression aligned with evidence
https://arxiv.org/list/math/new
It is natural to expect that if many pairs $(x_i,x_j)$ lie at distance close to $1$ from each other, then some clustering phenomenon must occur, implying that a significant number of these pairs are also very close to each other
https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries
https://arxiv.org/list/cond-mat.mes-hall/new
Together with metallic transport and a crystal-site density much lower than the doped carrier density, this supports metallic electron crystals in which a subset of carriers crystallizes
https://arxiv.org/html/2608.03494v1
com
1 Introduction
Modern large language models (LLMs) process text using subword tokenizers such as Byte-Pair Encoding (BPE) Sennrich et al
https://arxiv.org/html/2608.00426v1
This memory layer improves continuity, but it also gives attackers a durable channel: a poisoned memory can be written once, continuously retrieved in later tasks, promoted into shared memory, and reused by other agents
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2608.03411v1
com
Abstract
Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs), yet traditional probability-based metrics often fail to capture the model’s true epistemic state
https://arxiv.org/html/2608.02999v1
The construction does not decide what the Indus signs encode: it shows that the evaluated measures detect organization without being specific to language, and therefore cannot, on their own, establish encoded speech
https://arxiv.org/html/2508.02739v1
We pre-train Kronos using an autoregressive objective on a massive, multi-market corpus of over 12 billion K-line records from 45 global exchanges, enabling it to learn nuanced temporal and cross-asset representations
https://arxiv.org/html/2608.02665v1
Yet evaluating only the canonical prompt underestimates unsafe compliance: the union of unsafe outcomes across forms exceeds even the worst single form by 3
https://arxiv.org/html/2608.02942v1
At inference, the student’s early parallel commitments alter the context of later predictions, so the states it actually visits drift away from the supervised ones—precisely when step compression is most aggressive
https://arxiv.org/list/astro-ph.IM/new
It is generally known how various relativistic effects affect a given astrometric observation
https://arxiv.org/list/hep-ph/new
The contribution of the second branch in physical processes is strongly dependent on the initial momentum geometry: it vanishes for massless head-on fermions in a timelike background, whereas boosted nearly parallel configurations can generate an energy-enhanced correction
https://arxiv.org/html/2608.03153v1
0%, and correcting for differential selection into full-time wage employment with semiparametric, machine-learning, and bounds methods leaves it unchanged
https://arxiv.org/html/2401.02063v2
Furthermore, observational capabilities in this field are advancing at an almost unprecedented pace: for example, it is conceivable with the Cosmic Explorer experiment11 1 https://cosmicexplorer
https://arxiv.org/list/quant-ph/new
The suggested interpretation leads to differences in some outcome probabilities compared to previous analyses which, in principle, make it possible to falsify the suggested modifications or the previous analyses (or both)
https://arxiv.org/list/cs.IR/new
To handle it, KGD assigns them to separate parameter sets: a refreshable encoder owns behavioral knowledge, while a task learner reads contextualized encoder states through read-only cross-attention and writes task-specific geometry through Anchored Calibration Residual (ACR) orthogonal to the pretrained embedding