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How to Remove Claude Watermarks from Text, Code, and Files
Claude now marks AI-generated content. But it does not mark everything the same way. Anthropic currently uses embedded watermarks for text and signed C2PA provenance metadata for supported files. Code sits somewhere in between: it is still text, but its structure gives the watermark fewer places to work. I went into detail about Claude s watermarks [ ] The post How to Remove Claude Watermarks from Text, Code, and Files appeared first on Analytics Vidhya.
GxP-Agent: Process-DAG Topology for Reliable Clinical Trial Programming with LLM Agents
arXiv:2608.16890v1 Announce Type: new Abstract: Clinical trial programming -- transforming study protocols into analysis-ready datasets under CDISC standards -- is a bottleneck in regulatory submissions, yet LLM-based code generation fails catastrophically on this task: across 11 single-shot attempts with five frontier models, none produces a valid subject-level analysis dataset. We introduce GxP-Agent, a multi-agent system that encodes regulatory process ordering as a directed acyclic graph (DAG), decomposing monolithic dataset generation into 15 domain-specific nodes executed by worker agents with pharmaverse skill context, validation gates, and conditional retry. On CDISC-Bench, a new execution-based benchmark built from the FDA pilot submission CDISCPilot01 (254 subjects, 49 ground-truth ADSL variables), GxP-Agent with Claude Sonnet 4.6 achieves 100% structural match (49/49 variables, 254 correct records) across three independent runs, compared to 59.2% for the best retrieval-augme
FetchMan: Learning Visual Humanoid Loco-Manipulation Policies from Simulated Experiences
arXiv:2608.17027v1 Announce Type: new Abstract: Visual loco-manipulation policies that can generalize to novel scenes and objects have long been a goal of robotics research. However, today's data-hungry algorithms make collecting sufficient demonstrations a struggle for tabletop manipulation, and even more so for humanoids that must also walk and balance. Learning from simulated data and transferring that behavior to the real world, as is commonly done in locomotion, sidesteps this struggle, so we replicate that recipe for loco-manipulation. In doing so, we find that cloning synthetic demonstrations results in a low performance ceiling no matter the amount of training data. Reinforcement learning breaks through it, and refining the cloned policy with Flow-GRPO on a single sparse reward yields performance that synthetic behavior cloning cannot match. Together, these stages form our end-to-end sim-to-real pipeline spanning more than 150,000 scenes, which we use to train FetchMan. We eval
VLCP: Vision Language Control Policy Closed-Loop Code Replanning for Robot Manipulation
arXiv:2608.16978v1 Announce Type: new Abstract: Turning a frontier vision-language model into a robot policy usually means fine-tuning it to emit an action representation it never saw in pretraining, which throws away much of the reasoning that made the model worth reaching for. We go the other way and keep the VLM frozen. It writes the policy as a short Python control function, with no demonstrations and no fine-tuning. Writing that code once is open-loop, though. Existing closed-loop methods react at the wrong level: they retry a fixed policy or pick a different subtask, but never rewrite the code that failed. VLCP closes the loop where the failure actually lives, on the control code, within a single episode. Every $K$ steps the VLM re-observes the scene from multi-view RGB, proprioceptive state, and a state delta, then rewrites the control function from what it just saw, so a failure is caught before it compounds. We evaluate on a 57-task MuJoCo/RoboVerse sweep. This training-free p
Detecting and Discriminating Operator Misspecification in Hybrid PDE-Parameter Learning: a Reference-Free Instrument, with Discrimination Bounded In Sample
arXiv:2608.16925v1 Announce Type: new Abstract: We build an instrument that reads, from a single fit and with no oracle, whether the operator a hybrid PDE-parameter estimator postulates is wrong-and separates that from a merely unidentifiable parameter. On one self-adjoint parabolic inverse problem, an information-matrix statistic with plug-in scale and per-seed parameter has median 0.19 under correct specification, rejection rate $0.033$ against a pre-registered ceiling of $0.10$, and rises to $224$ and $85$ under two misspecifications, firing in every replicate. On a correctly specified but non-identifiable design it stays mute-$0.050$ at $n=200$, Clopper-Pearson $[0.024, 0.090]$-while a rank statistic collapses to zero at a pre-registered boundary $c_5^*=2.15\times10^{-3}.$ Two readings of one fit therefore separate the two failures across the three designs a deployable test reaches. That separation is the contribution; detection alone is a crowded flank. In sample it is a bound, ou
Proactive Road Safety Intervention in Australia: Predicting Risky Driving Hotspots from Connected Vehicle Data
arXiv:2608.16913v1 Announce Type: new Abstract: Road safety monitoring has historically been reactive, relying on crash-record analysis after fatalities and injuries have already occurred. Proactive identification of high-risk locations and dangerous driving behaviour before incidents occur is a critical but underexplored challenge. This paper addresses this gap using connected vehicle telemetry data from Greater Sydney, Australia, to detect and forecast near-miss risky driving events at the Local Government Area (LGA) level. Risky driving is quantified through g-force thresholds (hard braking >0.6g, harsh cornering >0.47g, harsh acceleration >0.5g), and spatio-temporal heatmaps are constructed to identify high-risk zones. Eight predictive models are benchmarked across three families: ensemble learning (Random Forests, XGBoost, LightGBM), deep learning (LSTM, N-BEATS), and classical time-series methods (ARIMA, Exponential Smoothing, Prophet). ARIMA achieves the lowest mean absolute err
Runtime Governance for Agentic AI: Action-Boundary Control with Trusted Provenance and Fail-Closed Execution
arXiv:2608.16891v1 Announce Type: new Abstract: Agentic AI systems request tool actions that can modify files, send messages, launch jobs, or change workflow state. This shifts the safety problem from harmful text generation to harmful operational side effects. Prompt-level governance can shape model behavior, but it does not create an execution boundary. We introduce Aegis, a runtime governance system that treats model outputs as action proposals and mediates them through a trusted decision layer before tool execution. The model proposes; the trusted runtime decides. Aegis evaluates proposals against active policy state, resolves provenance server-side, fails closed under uncertainty, and routes selected cases through Senate-style settlement, a quorum- based non-unilateral authorization path. We evaluate Aegis on a repeated sandbox corpus spanning five run families, 42 tasks, three conditions, and ten repeats per family. Across 6,300 rows, prompt-policy conditioning produced 79 risky
Linux Kernel Got “Too Big” from AI! Linus: AI Finds Small Bugs, but It’s the “New Normal”
Continue reading on CodeElevation »
Building an Event Registration System That Never Loses a Guest
Building an event registration process that validates, confirms, and waitlists attendees on its own.Continue reading on Medium »
GLM-5.3 hits the API at $1.4/$4.4 per million tokens
After a stunning debut last week with cyber capabilities so advanced they reportedly found a previously undetected vulnerability in Cursor, GLM-5.3, the new frontier open source language model from Chinese startup z.ai, has now hit the application programming interface (API) — allowing developers the ability to build atop it and plug it into their agents and applications. Developers who previously subscribed to a GLM Coding Plan are currently limited to the OpenAI Chat Completions-compatible protocol. Z.ai said it plans to make the model s weights openly available, but a precise date and licensing remain to be seen. On the API, the price is unchanged from GLM-5.2: $1.40 per million input tokens and $4.40 per million output tokens. Cached input costs $0.26 per million tokens, while Z.ai currently lists cached-input storage as free for a limited time. That means developers can move to the new generation without taking a higher posted per-token rate from Z.ai, even as the company claims s
AI and Humans Are Way More Alike Than You Think
We trained it on us. It s starting to show.Continue reading on Medium »
How to Get Your Business Found on ChatGPT, Gemini & Perplexity | GEO Guide 2026
Learn how Indian businesses can rank on ChatGPT, Gemini, and Perplexity with this complete GEO (Generative Engine Optimization) guide Continue reading on Medium »