I’m new to RL and new to mujoco, so I have no idea what variables i should tune. Here are the variables ive rewarded/penalized: I’ve rewarded the following: + r_upright + r_height + r_vx + r_vy + r_yaw + r_still + r_energy + r_posture + r_slip and I’ve placed penalties on: p_vy = w_vy * vy^2 p_yaw = w_yaw * yaw_rate^2 p_still = w_still * ( (vx^2 + vy^2 + vz^2) + 0.05*(wx^2 + wy^2 + wz^2) ) […]
Continual learning (CL) on edge devices requires not only high accuracy but also training-time efficiency to support on-device adaptation under strict memory and computational constraints. While prompt-based continual learning (PCL) is parameter-efficient and achieves competitive accuracy, prior work has focused mainly on accuracy or inference-time performance, often overlooking the memory and computational costs of on-device training. In this paper, we propose CPS-Prompt, a critical patch-aware sparse prompting framework that explicitly targets training-time memory usage and computational cost by […]
arXiv:2602.21252v1 Announce Type: new Abstract: Security monitoring systems typically treat anomaly detection as identifying statistical deviations from observed data distributions. In cryptographic traffic analysis, however, violations are defined not by rarity but by explicit policy constraints, including key reuse prohibition, downgrade prevention, and bounded key lifetimes. This fundamental mismatch limits the interpretability and adaptability of conventional anomaly detection methods. We introduce INTACT (INTent-Aware Cryptographic Traffic), a policy-conditioned framework that reformulates violation detection as conditional constraint learning. Instead of […]
Stateful MCP client capabilities on Amazon Bedrock AgentCore Runtime now enable interactive, multi-turn agent workflows that were previously impossible with stateless implementations. Developers building AI agents often struggle when their workflows must pause mid-execution to ask users for clarification, request large language model (LLM)-generated content, or provide real-time progress updates during long-running operations, stateless MCP servers can’t handle these scenarios. This solves these limitations by introducing three client capabilities from the MCP specification: Elicitation (request user input mid-execution) […]
Finetuning allows us to adapt pretrained LLMs in a cost-efficient manner. But which method should we use? This article compares different parameter-efficient finetuning methods for the latest top-performing open-source LLM, Falcon. Using parameter-efficient finetuning methods outlined in this article, it’s possible to finetune an LLM in 1 hour on a single GPU instead of a day on 6 GPUs.
We investigate whether temporal embedding models trained on longitudinal electronic health records can learn clinically meaningful representations without compromising predictive performance, and how architectural choices affect embedding quality. Model-guided medicine requires representations that capture disease dynamics while remaining transparent and task agnostic, whereas most clinical prediction models are optimised for a single task. Representation learning facilitates learning embeddings that generalise across downstream tasks, and recurrent architectures are well-suited for modelling temporal structure in observational clinical data. Using the […]
I really think “give AI total control of my computer and therefore my entire life” is going to look so foolish in retrospect that everyone who went for this is going to look as dumb as Jimmy Fallon holding up a picture of his Bored Ape — Christopher Mims, Technology columnist at The Wall Street Journal Tags: ai, security
We study reinforcement learning for revenue management with delayed feedback, where a substantial fraction of value is determined by customer cancellations and modifications observed days after booking. We propose emph{choice-model-assisted RL}: a calibrated discrete choice model is used as a fixed partial world model to impute the delayed component of the learning target at decision time. In the fixed-model deployment regime, we prove that tabular Q-learning with model-imputed targets converges to an $O(varepsilon/(1-γ))$ neighborhood of the optimal Q-function, […]
arXiv:2601.09869v1 Announce Type: new Abstract: Anthropomorphisation — the phenomenon whereby non-human entities are ascribed human-like qualities — has become increasingly salient with the rise of large language model (LLM)-based conversational agents (CAs). Unlike earlier chatbots, LLM-based CAs routinely generate interactional and linguistic cues, such as first-person self-reference, epistemic and affective expressions that empirical work shows can increase engagement. On the other hand, anthropomorphisation raises ethical concerns, including deception, overreliance, and exploitative relationship framing, while some authors argue that […]
arXiv:2601.07998v1 Announce Type: new Abstract: Understanding human visual search behavior is a fundamental problem in vision science and computer vision, with direct implications for modeling how observers allocate attention in location-unknown search tasks. In this study, we investigate the relationship between Gabor-based features and gray-level co-occurrence matrix (GLCM) based texture features in modeling early-stage visual search behavior. Two feature-combination pipelines are proposed to integrate Gabor and GLCM features for narrowing the region of possible human fixations. The pipelines […]