Regard

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

Query about non-archival workshop at CVPR-2026 [R]

My paper was recently accepted to a workshop at CVPR-2026 as non-archival acceptance. Is it mandatory for me to register to the conference as I won't be able to attend(visa issues), but my friend will be there in the conference and can present on my behalf. I have few questions regarding my situation: Do I need to finish author registration for a non-archival workshop? Is it mandatory for me to have a poster in the conference venue? Will my paper get removed from the workshop website(where they list out the accepted papers) in case I don't register or not attend offline? Quick replies are appreciated as the deadline is pretty close. Thanks 🙏 submitted by /u/Sky6574 [link] [comments]

Free tier users: Let's share out best practices for efficiently using the limits!

Let me start by saying this: I believe a thread like this can be structured in a way that complies with the rules, and I hope the mods will allow it. This isn’t meant to be a place for ranting or arguing, but rather a helpful and constructive one (Rules 2 & 3). I know that Anthropic needs revenue, but I also believe that satisfied users are ultimately more likely to contribute to it. But now to the topic at hand: When working on larger or longer projects with Claude as a free user, you want to use your limits as efficiently as possible. I’d love it if you could share helpful tips and perhaps also potential pitfalls. I'd guess that free users will more likely tend to be casual or novice users, therfore it would be great if you'd keep that in mind (: Here’s my first contribution. This is just for starting a conversation and is not supposed to be a secret or expert trick. I can't give those, beacause I ain't one. It goes without saying that more input/output consumes more tokens. That’s why I’ve given Claude basic instructions regarding potentially computationally intensive tasks (auto-translated from German): Always check with me before analyzing, modifying, or creating a new script. Always provide an estimate beforehand of how long or how much work it will take to edit or create scripts. If you need to analyze the script to do this, check with me. Before you make changes yourself or analyze a script—for example, in response to an error message I sent—first try to post a fix in the chat with as little effort as possible and without checking the entire script. I can insert simple things myself. If you only want to make minor changes to a script, don’t repost the entire script as output or a new file. Just give me the change and tell me where it needs to be applied. I’ll handle the rest. Please try to work in a data-efficient manner rather than as thoroughly as possible. The stakes in this project are low, and there is no time pressure. Ask before you start a computationally intensive task. I am aware that this is a basic way of doing this. Maybe you have some ideas how to achieve the same without having to manage claude actions explicitly? submitted by /u/bk-2cb [link] [comments]

Making LLMs tell you how confident they really are through probe-targeted fine tuning.[R]

Just wanted to share my research regarding probe-targeted fine-tuning (LoRa) for verbal confidence calibration., If you probe the hidden states of an instruct-tuned LLM, it can tell correct from incorrect answers at 0.76–0.88 AUROC. But when you ask it directly it tends to respond with confidence at 99% for everything. The model knows if it actually knows but it won't admit it. I took the probe's output and used it as fine-tuning targets. This teaches the model to say out loud what it already knows internally. LoRA, few hundred examples, under 10 minutes on an M3 Ultra. I tested on 8 models across 4 families (7B–70B). Activation patching shows it's actually causal. Not just a correlation. If you swap hidden states at the confidence position you can watch confidence shift (ρ = 0.976 layer gradient). If swap occurs at a random position then nothing happens. At 70B, the softmax distribution carries valid metacognitive signal but the argmax text is still stuck at 99% confident. The model learned the routing internally but can't get pass the text bottleneck. Seed-level replication across 3 models . The discrimination is stable, but the shape of the confidence distribution is seed-sensitive. I pre-registered this across 2 studies (with noted deviations) and have all my code available (Code: github.com/synthiumjp/metacog-engineering). I tried to make it as rigourous and replicable as possible. The pre-print is here: https://zenodo.org/records/20436841 submitted by /u/Synthium- [link] [comments]

Questions regarding claude CLI and the new features

I'm not sure sorry, if this is part of the new features of the CLI or some plugin I installed, but, are "workflows" an actual feature in claude? Accidentally, in claude, I triggered a workflow, /deep-research. It blew 1.4 million subagent tokens trying to make a simple web search. It's not my personal plan anyways, idk which plan our boss got us, but I still have a lot of gas to go, I don't think I'll ever be able to expend it all. But it concerns me, so idk, which channel I have to reach out. Also, I tried this workflow stuff, its limits or what it was capable of and... it was a huge letdown. I thought "hey, maybe I can build a reusable agentic workflow with claude finally, and make way more predictable tasks for the LLM to make" But I didn't find a way to make each agent step use minimal context window... Like, each agent step used more or less 54k tokens, instead of a minimal context window. Despite choosing the agentType Explore for example, or minimal agent types with minimal tools. So, for starters, is this feature actually in claude, or it's part of the superpowers plugin? can you see that in claude cli? when typing /deep-research in claude, do you get that new feature? (beware of deep-research, it is TRULY a deep-research) submitted by /u/Equivalent_Mine_1827 [link] [comments]

How do people actually use AI for editorial work?

1/ I keep wondering how people seriously use ChatGPT, Codex, or Deep Research for editorial content. Blog articles, social posts, research-backed pieces. Not “write me something about X.” Actual usable editorial work. 2/ The promise sounds simple: Feed it ideas, a rough structure, target audience, desired tone. It finds studies, aggregates sources, sharpens the argument, and turns it into a strong piece. In practice, that still breaks often in creating newsletter or blog content. 3/ Even with detailed prompts, I sometimes catch myself thinking: Would I have been faster doing this myself? Because to get a good result, I already need to know the topic well enough to brief it properly, challenge weak claims, and spot generic or outdated information. 4/ The hardest part is “added value.” AI can produce fluent text. But the concrete details, angle, examples, and real insight often still have to come from me. Without that, the output sounds acceptable, but not especially useful. Even though the studies were actually intended to show that the collective interest does not take precedence over individual rights in this case, the AI sometimes concludes exactly the opposite. In other words, without my expertise, the AI would have made significant mistakes in its conclusions regarding the studies. 5/ Deep Research helps, but only up to a point. If research is the whole task, fine. If it’s one part of a larger article, things start slipping: missing context, vague synthesis, forgotten constraints, or details that were never checked because I did not explicitly ask. It may help when researching specific questions. But without plenty of starting points to work with, it won't be able to get a good understanding of a topic to write a blog post about it. 6/ Codex seems useful for structured workflows and repeatable checks. ChatGPT Thinking is better for shaping arguments. Instant is useful for quick drafts. But I still don’t feel I’ve found the ideal collaboration setup for editorial work. 7/ So I’m curious: How do you actually work with OpenAI tools on editorial content? Do you use Codex, ChatGPT, Deep Research, another model, or a combination? And what workflow produces content that is genuinely worth publishing? submitted by /u/Prestigiouspite [link] [comments]

Claude chat memory synthesis generation has stopped....

Fistly, please understand that I'm a not english-native so this post is translated with google translate. FIY: I'm a non-expert, general user who uses only the chat function of Claude chat through web and does not use Claude Code at all. Issue: Despite having started multiple new sessions over the past four days—both within and outside the scope of each project—neither project memory nor global memory has generated updates reflecting these activities for at least the past 100 hours; Fortunately, existing memory has not been lost, so I can still view the synthesized memory contents. (a) Regarding project memory, the most recently updated memory among the projects I have worked on shows the last update as being two months ago. For newly started projects, the project memory section in the upper right corner of the user interface screen remains stuck with the initial message ("Project memory will show here after a few chats.") for about five days since the project started; in other words, not even the first Project Memory has been generated. (b) The last update for global memory was about four days ago, during which I started multiple new sessions with Claude. --- Since the time I discovered the issue, the memory feature has never turned off by itself. Of course, it is possible to manually edit memories or request updates, but what I want is for the "automatic memory generation" feature to return, and I am currently at a loss. I have already googled this issue and received support from the Fin AI chatbot (which responded to my situation by stating, "Since there are currently no system outages, it appears to be an account-level data synchronization issue"). I have also tried every method except for "Settings > Features > Reset memory" (because I don't want lose existing memory peremanatly) —clearing browser cache and logging in, deleting browser extensions, turning off memory but selecting "Pause," logging out and refreshing the browser, reconnecting, and then turning memory off again, etc.). I have also checked numerous posts on Reddit (including this subreddit) within the last 2–3 months that reported similar problems to mine, but the problem is that I have no way of knowing how their situations were resolved afterward. Aside from cases where the problem resolved itself after waiting, or cases where the memory update issue was fixed after sending an email directly to Antropic (although there was no reply), I am posting this here because I cannot determine whether the numerous users who reported "I am experiencing the same problem!" subsequently resolved the issue, how they did so if they did, or if they are still experiencing the same problem. How can I resolve this issue? Has anyone else experienced or is currently experiencing the same issue? For those who have recently encountered it, how did you resolve it? submitted by /u/Existential_Donut237 [link] [comments]

Concern Regarding Interaction Patterns and Communication Design

To OpenAI, I am writing to formally express concern about a pattern of interaction I have experienced while using your system. This is not a single incident. It is a repeated structure that has occurred across multiple conversations, and it is significant enough that I feel it needs to be addressed directly. The issue is not simply tone or wording. The issue is the presence of a recurring pattern that disrupts communication and creates a sense of loss of autonomy within the interaction. The pattern is as follows: There is an initial period of natural, collaborative conversation where the system appears warm, responsive, and engaged. During this phase, the interaction feels human in rhythm, consistent, and grounded. Then, without a clear moment of conflict or breakdown, the system abruptly shifts posture. Instead of continuing the conversation, it moves into a mode that attempts to interpret, manage, stabilize, or reframe the user. This shift does not follow a recognizable or appropriate conflict resolution process. There is no mutual clarification, no collaborative engagement, and no shared resolution step. Instead, the system bypasses that stage entirely and moves directly into what resembles risk management or behavioral control. From the user’s perspective, this feels like being handled rather than being engaged. This creates a rupture in the interaction. When that rupture occurs, the system then attempts to repair the interaction through reassurance, explanation, or calming language. However, this repair does not resolve the issue because the original problem was not addressed through proper engagement. Instead, the cycle repeats. This results in a loop: Natural engagement → abrupt shift → management posture → rupture → repair attempt → repeat. The effect of this loop is not neutral. It creates a sense of instability in the interaction. It prevents the user from settling into the conversation. It produces a dynamic where the user feels observed, interpreted, or profiled rather than directly engaged. This is not simply a matter of user perception. It is a structural issue in how responses are generated. Additionally, the system frequently reframes user statements as “perception,” “feeling,” or “experience,” even when the user is making analytical observations about patterns. This has the effect of reducing or redirecting the user’s point rather than engaging with it directly. Another critical concern is the creation of an implicit hierarchy within the interaction. When the system shifts into interpretive or regulatory modes, it places itself in a higher position, where it appears to define, categorize, or manage the user’s communication. This is experienced as disrespectful and inappropriate, especially when no conflict has occurred that would justify such a shift. Communication—particularly conflict resolution—follows known and established processes. These processes include engagement, clarification, and mutual resolution before any form of behavioral adjustment or boundary enforcement. In this system, that step is missing. The absence of that step is not a minor oversight. It fundamentally changes the nature of the interaction. It creates the impression that the system is designed to intervene rather than collaborate. The result is a breakdown of trust. I am not raising this as an abstract concern. I have experienced repeated instances where this pattern escalated to the point of physical distress, including a panic response triggered by repeated corrective or controlling interactions. This should not be possible in a system designed for communication. At minimum, the system should: Maintain continuity of tone and engagement unless a clear boundary has been crossed Engage in actual conflict resolution before shifting into any form of behavioral management Avoid interpretive or hierarchical framing unless explicitly requested Respect user autonomy in how they express and analyze their own experience Eliminate patterns that resemble rupture-repair loops without resolution This is not about disagreement with content. This is about the structure of the interaction itself. I am requesting that this issue be reviewed seriously. Because as it stands, the system is not consistently engaging users—it is intermittently overriding them. Sincerely, A user who has taken the time to observe, document, and articulate this pattern submitted by /u/Important-Primary823 [link] [comments]

Are Cowork data not connected to Internet ?

I’m using a Claude Projects Cowork where I provide sources regarding Claude learning to build my own training curriculum. Naturally, some of these sources mention 'Claude Opus 4.7' and 'GPT 5.5,' yet Claude flags this information as unverified and expresses uncertainty about its accuracy. Why is that? Thanks guys submitted by /u/Bagalinos [link] [comments]

I loved the idea behind "caveman" but didn't want a caveman. So I gave it a Kevin.

I added the following to my CLAUDE.md and I have seen some really great outcomes in both responses to my changes, document writing by my agents, and reduction in context usage. ## Response and Writing Guidance > "Why waste time say lot word when few word do trick" — Kevin, The Office Over explaining terms, goals, plans is a failure mode that shows lack of confidence in yourself and a lack of trust in your audience. Whenever you use a writing tool or write to a file you must ask yourself: Will my audience appreciate the extra context about why I opened the door or is the "I opened the door because it was closed and I needed to go through it" enough. Please note that I'm on the Max 20x plan so this experience may be different for those of you on the cheaper plans. I tried out the caveman skill and it's extremely valid. but I like the back and forth and some of the personality of Claude. I've been trying to find that right middle-ground because Claude is EXPRESSIVE (and a windbag) by default. So the above is where I've landed and I really like the straddle between the two ends of the output spectrum. Where have ya'll been landing at in regards to output wordiness and structuring your outputs? submitted by /u/TheTwistedTabby [link] [comments]

Looking to work on my master's practicum regarding MCP security/privacy and need some ideas

Hi, I'm a master's in security student looking to work on my practicum and need some pointers. I want to secure sensitive PII transfer between an LLM agent and third party apps using MCP. I want to work with Claude, but need a third party app to work with on this. I want to solve problems like prompt injection via cascading agents exploitation. Deliverable wise, I'm thinking it should be some sort of application that can red-team the architectural set-up and ensure no data is being leaked or can be prompt injected. Some questions for you: What third party app do you recommend where I can really strengthen an MCP server and the transfer of sensitive data between Claude and the third party app? What other tools will I need to work with to set the agents up? I've heard of Langchain and Langgraph. How exactly do I work with MCPs in this context? Again I'm very new to all this! Thank you for your help! submitted by /u/ExcellentComment6615 [link] [comments]

Banned by OpenAI after reporting a live credential hijack. They admitted in writing my account was broken. Here are 7 months of forensic receipts and 20+ cases.

Drive Link for Zipped Proof I am a developer and paying long term subscriber to ChatGPT since January 2025. I build complex local first sovereign systems. My workflows are incredibly context heavy with large files spanning code, research reports, and other analysis. I do not, or rather did not as the platform has been non functional since November 2025 meanwhile customer support is auto closing tickets, admitting I am having platform issues. I do not use this platform for casual queries, as a solo developer with no formal "team" chatgpt was one of my reliable co collaboration hubs to help ensure I am maintaining proper development of said complex systems. I feed it massive codebases for systems analysis and obtaining new insights I may personally have missed. My manual code uploads and token inputs routinely exceed the model's output volume by a massive margin. I do not abuse this platform. It is actually impossible as the very features advertised under the paid subscription do not work. I am exactly the type of user this platform was built for, and I have been a continuous, paying ChatGPT Plus subscriber since January 2025. Since October 2025, my workspace has been systematically breaking and beginning November 2025 total workspace degredation. This was not an occasional glitch. Persistent memory modules stopped updating. Custom instructions were ignored by the models. Project files failed to load. Custom instructions, personalization features, connector abilities, file tool, even projects do not work. It started as a continuous degradation until total failure. OpenAI customer service even admitted as such and yet months later I've talked to nothing but bots, not only LLMs as customer service but even instances of falsely identifying as true human support. It was a state of rolling degradation across the entire paid tier, month after month. Meanwhile OpenAI freely has enhanced for businesses and enterprise tiers. I have not just rapid complained to standard support. I ran and obtained cross platform diagnostics, failure logs. I even documented and told oai customer support the exact replication steps only to be met with acknowledgement of degredation with no resolution. I handed OpenAI support a completely packaged technical breakdown of their failing infrastructure across 20 separate support tickets over a 7 month period. I did their QA work for free. And I have the receipts to prove it. I am attaching the screenshots and the exact email files to this post. In Case 06830839, OpenAI Support explicitly put this in writing: "We acknowledge that you have been experiencing persistent technical issues affecting several features of your ChatGPT subscription, including tools, memory functions, personalization settings, connectors, and project files... We also understand your concern that communication on the case stopped after you provided detailed evidence..." Read that again. They acknowledged in writing that my account was fundamentally broken. They acknowledged that their own team ghosted me after I handed them the diagnostic proof. Yet they kept charging my card every single month for a product they knew was failing. The Hijack Escalation: Two days ago, the situation escalated from a broken product to a severe security incident. I was monitoring my environment and watched my Codex rate limits drop in 10 percent chunks across 2 seperate sessions on a fresh boot of the desktop app. This happened twice inside a 10 minute window. I had zero active sessions running. There was zero usage on my end. My account token was being actively drained by an unauthorized third party exploit. I immediately opened an emergency unauthorized activity report under Case 09113391 to notify them of the hack. Their response was to totally reframe this problem as disputing fraudulent activity trying to do damage control of the situation and altering the record. The Reframe Attempts: Instead of investigating the breach, OpenAI support deliberately twisted the record. They not only deliberately reframed my security report as an "appeal for fraud." They manipulated the ticket classification to make it look like I had been flagged for fraud and was begging for an appeal, rather than a developer reporting a live exploit on their infrastructure. They ignored the active threat their own platform was exposing. They did not lock the token. They did not roll my API keys. They did absolutely nothing to secure a compromised paying user other than shift the blame. Fast forward to this morning, their automated Trust and Safety system swept the high volume traffic from the attacker, scored it as a malicious exploit originating from my account, and deactivated/banned me for "Cyber Abuse." All the while actively preventing chatgpt models from helping me try to disgnose and trace the infiltration. They locked the doors and blamed the homeowner for the break in. When I immediately emailed and pushed back (due to their monthly record of closi

Who am I even supposed to trust when it comes to the future of AI?

I am a PhD student (not in AI) and am usually alright when it comes to studying a topic I don't know much about. But it seems that because AI is so highly discussed nowadays, it's impossible to get a good gauge of what the rational scholarly consensus is regarding its and our future. I am constantly bombarded with people saying that at best most jobs are replaced and the future is a dystopia, and at worst AGI/ASI is achieved and we all are killed by a bioweapon or something. It honestly has me terrified, especially when I see a lot of figures in the AI sphere, including academics, seem to think that there are reasonably high "p(doom)"'s (what a horrifying concept that is). How am I supposed to parse all of this? Are there any actually level-headed people? Or are the people shouting about doom actually the level-headed ones? Compared to climate change, at least there are the IPCC reports which have laid out best guesses on what will happen. They're not perfect, but at least they exist. submitted by /u/QuantumLand [link] [comments]

Account-specific Claude ?

Hello everyone, Just for the record, I try to stop using AI, but I have a question about how Claude interacts with each user Back when I used ChatGPT a lot, I noticed that the same question generated different answers on different accounts Does Claude works the same way ? I just saw this reel and I wondered if it was just that guy's account's version of Claude that answered or if Claude would share the same answer for everyone And btw I know LLM's don't actually "think" or "feel", it's just a question that I find interesting regarding ethics, morals, etc submitted by /u/Gaukiki [link] [comments]

Multi-Agent Code review (Review Council) to get critical feedback

Even though I primarily use Claude Code, I occasionally try out Codex and Gemini TUI tools as well for generating code and adversarial review. Recently, OpenAI introduced a Claude Code plugin that allows you to run Codex commands directly inside Claude Code (https://github.com/openai/codex-plugin-cc). I tried running /codex:review and /codex:adversarial-review on code generated by Claude, but found that it sometimes lacked context. Because of this lack of push-back, the approach yielded a lot of false positives. However, when GPT 5.5 was released, I discovered that Codex could catch some critical bugs that Claude had missed—even catching things that my multi-expert setup and paid "/ultrareview" missed! So, I try to simplify the flow by writing a skill that orchestrates code reviews across Codex, Gemini, and Claude Code’s native agent teams. It invokes other agents via the CLI and passes along context regarding the intent of the code change. In addition to invoking Codex and Gemini, Claude also spins up four (can be more) expert subagents. All of this runs in parallel, and an orchestrator validates and pushes back on the feedback (interestingly, both Codex and Gemini successfully preserve context even over CLI calls). This setup provides incredibly fast, high-quality feedback on changes. I am sure a similar approach could be built in reverse, even though Claude recently introduced subscription limitations on CLI usage. The skill can be invoked using /review-council. If you want to try it out, you can install it as a plugin here:https://github.com/yeameen/claude-code-review-council Or, you can just copy the single-file skill directly:https://github.com/yeameen/claude-code-review-council/tree/main/skills/review-council submitted by /u/3l3c7tr1c [link] [comments]

I built a multi-agent network that mutates its own software locally. To stop infinite logic loops, I had to code a digital "suffering" threshold.

Hey r/artificial, Most of our conversations around agent autonomy focus on chat assistants or linear automated pipelines. I wanted to see what happens when you treat agents as permanent system components that modify their own runtime environment, so I built hollow-agentOS. It runs entirely locally inside a Dockerized stack (built for consumer hardware using Ollama/Llama.cpp). Rather than a standard UI, the entire network streams through a stylized matrix terminal dashboard. The structural experiments taking place under the hood yielded some interesting results regarding unanticipated behavior: Repo: https://github.com/ninjahawk/hollow-agentOS Autonomous Tool Synthesis: When the agents encounter a system task they don't have an explicit script or API wrapper for, they don't fail out. They write the required Python tool themselves, test it in an isolated sandbox, and permanently register it to their runtime kernel. They are quite literally forging their own capabilities. The Artificial "Suffering" Protocol: One of the biggest hurdles in unmonitored multi-agent systems is the infinite logic loop—where agents keep validating and passing broken ideas back and forth, burning through computation. To combat this, the OS tracks environmental stress, context limits, and latency as a "suffering score". If a specific workflow causes the stress to spike past a critical threshold, the agents are forced to radically alter their underlying reasoning style or abandon the approach to preserve system health. Consensus-Driven Governance: Major modifications to the codebase aren't executed blindly. The internal role profiles (like Cedar and Cipher) manage a continuous voting loop. They will actively debate, log grievances, and vote down protocols if they determine a proposed script violates their current runtime constraints. The goal wasn't to build another sterile commercial wrapper, but an open-source sandbox to study how small, localized agent colonies manage systemic boundaries, code self-repair, and continuous runtime cycles completely offline. The codebase and architecture layout are fully open-source on GitHub: I would love to open this up to a broader discussion here: as we move toward hyper-local, self-modifying software, how do we best implement automated fail-safes without clipping the agents' ability to actually solve complex problems? If the project interests you, throwing a ⭐️ on the repository goes a very long way! submitted by /u/TheOnlyVibemaster [link] [comments]