📌 TOPINDIATOURS Update ai: The Winklevoss Twins’ Crypto Company Is in Crisis After

America’s favorite twins are struggling these days, as Bitcoin’s crash continues to hammer their decade-old crypto exchange, Gemini Space Station Inc.

With Bitcoin down by over 40 percent since its record highs in fall of 2025, Bloomberg reports that shares for Cameron and Tyler Winklevoss’ crypto marketplace have collapsed by more than 80 percent from their 2025 highs, wiping out over $3 billion in market value. The situation seems to be rattling the enterprise to its core, coming the same week that Gemini moved to kick its chief operating officer, financial officer, and legal officer to the curb.

Earlier in February, the dynamic duo — both portrayed in 2010’s “The Social Network” by Armie Hammer, back before we knew he was a cannibalism fetishist — announced Gemini was slashing at least a quarter of its total workforce and drawing down operations across the UK, Europe, and Australia. As it happened, the company went beyond its stated 25 percent reduction in headcount as it began cutting even more jobs in the US, Bloomberg reported.

A peek under the hood at Gemini reveals that its spending has been growing far faster than its revenue. According to a report filed last week, the company’s annual expenses grew to around $525 million in 2025, up from $308 million the year before. Net revenue, however, only came out to around $170 million, mostly driven by a new credit card scheme, and well short of what the company needs to break even, let alone carve out a profit.

Keep in mind, even though Bitcoin started to tumble late into 2025, the crypto market had a banner year as a whole. If the Winklevii couldn’t make hay while the sun was shining on the best crypto market ever seen following the election of industry super-ally Donald Trump, it tracks that they wouldn’t fare well when the wildly volatile digital currencies inevitably experience a period of bust.

All in all, the continual collapse of one of the longest-standing crypto exchanges is a major red flag for the “industry” writ large, and financial analysts aren’t pulling their punches.

“The biggest issue here is that Gemini’s management team placed a big bet on the crypto bull market run continuing through 2027 and instead crypto asset prices have cratered,” analysts at the investment bank Truist Securities wrote in a note Tuesday viewed by Bloomberg. “Their strategy needs to change.”

As for the billionaire twins, their work is just getting started. Bloomberg reports that Cameron Winklevoss — Gemini’s president — will be assuming roles previously overseen by the erstwhile COO, and we can only assume that Tyler, the CEO, will be adding more to his plate as well.

The two had previously taken Gemini public on the Nasdaq Index in September, dazzling investors right before things began to turn ugly for crypto in October.

As Truist analyst Matthew Coad told Bloomberg: “they made the wrong bet at the wrong time.”

More on crypto: If Bitcoin Keeps Tanking, It Could Cause a “Death Spiral” for the Entire Economy

The post The Winklevoss Twins’ Crypto Company Is in Crisis After the Bitcoin Crash appeared first on Futurism.

🔗 Sumber: futurism.com

📌 TOPINDIATOURS Update ai: Which Agent Causes Task Failures and When?Researchers f

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Meet the authors
Institutions: Penn State University, Duke University, Google DeepMind, University of Washington, Meta, Nanyang Technological University, and Oregon State University. The co-first authors are Shaokun Zhang of Penn State University and Ming Yin of Duke University.

In recent years, LLM Multi-Agent systems have garnered widespread attention for their collaborative approach to solving complex problems. However, it’s a common scenario for these systems to fail at a task despite a flurry of activity. This leaves developers with a critical question: which agent, at what point, was responsible for the failure? Sifting through vast interaction logs to pinpoint the root cause feels like finding a needle in a haystack—a time-consuming and labor-intensive effort.
 
This is a familiar frustration for developers. In increasingly complex Multi-Agent systems, failures are not only common but also incredibly difficult to diagnose due to the autonomous nature of agent collaboration and long information chains. Without a way to quickly identify the source of a failure, system iteration and optimization grind to a halt.
 
To address this challenge, researchers from Penn State University and Duke University, in collaboration with institutions including Google DeepMind, have introduced the novel research problem of “Automated Failure Attribution.” They have constructed the first benchmark dataset for this task, Who&When, and have developed and evaluated several automated attribution methods. This work not only highlights the complexity of the task but also paves a new path toward enhancing the reliability of LLM Multi-Agent systems.

The paper has been accepted as a Spotlight presentation at the top-tier machine learning conference, ICML 2025, and the code and dataset are now fully open-source.

Paper：https://arxiv.org/pdf/2505.00212
Code：https://github.com/mingyin1/Agents_Failure_Attribution
Dataset：https://huggingface.co/datasets/Kevin355/Who_and_When
 
 
Research Background and Challenges
LLM-driven Multi-Agent systems have demonstrated immense potential across many domains. However, these systems are fragile; errors by a single agent, misunderstandings between agents, or mistakes in information transmission can lead to the failure of the entire task.

Currently, when a system fails, developers are often left with manual and inefficient methods for debugging:
Manual Log Archaeology : Developers must manually review lengthy interaction logs to find the source of the problem.
Reliance on Expertise : The debugging process is highly dependent on the developer’s deep understanding of the system and the task at hand.
 
This “needle in a haystack” approach to debugging is not only inefficient but also severely hinders rapid system iteration and the improvement of system reliability. There is an urgent need for an automated, systematic method to pinpoint the cause of failures, effectively bridging the gap between “evaluation results” and “system improvement.”

Core Contributions
This paper makes several groundbreaking contributions to address the challenges above:
1. Defining a New Problem: The paper is the first to formalize “automated failure attribution” as a specific research task. This task is defined by identifying the failure-responsible agent and the decisive error step that led to the task’s failure.
2. Constructing the First Benchmark Dataset: Who&When : This dataset includes a wide range of failure logs collected from 127 LLM Multi-Agent systems, which were either algorithmically generated or hand-crafted by experts to ensure realism and diversity. Each failure log is accompanied by fine-grained human annotations for:
Who: The agent responsible for the failure.
When: The specific interaction step where the decisive error occurred.
Why: A natural language explanation of the cause of the failure.

3. Exploring Initial “Automated Attribution” Methods : Using the Who&When dataset, the paper designs and assesses three distinct methods for automated failure attribution:
– All-at-Once: This method provides the LLM with the user query and the complete failure log, asking it to identify the responsible agent and the decisive error step in a single pass. While cost-effective, it may struggle to pinpoint precise errors in long contexts.
– Step-by-Step: This approach mimics manual debugging by having the LLM review the interaction log sequentially, making a judgment at each step until the error is found. It is more precise at locating the error step but incurs higher costs and risks accumulating errors.
– Binary Search: A compromise between the first two methods, this strategy repeatedly divides the log in half, using the LLM to determine which segment contains the error. It then recursively searches the identified segment, offering a balance of cost and performance.

Experimental Results and Key Findings 
Experiments were conducted in two settings: one where the LLM knows the ground truth answer to the problem the Multi-Agent system is trying to solve (With Ground Truth) and one where it does not (Without Ground Truth). The primary model used was GPT-4o, though other models were also tested. The systematic evaluation of these methods on the Who&When dataset yielded several important insights:
– A Long Way to Go: Current methods are far from perfect. Even the best-performing single method achieved an accuracy of only about 53.5% in identifying the responsible agent and a mere 14.2% in pinpointing the exact error step. Some methods performed even worse than random guessing, underscoring the difficulty of the task.
– No “All-in-One” Solution: Different methods excel at different aspects of the problem. The All-at-Once method is better at identifying “Who,” while the Step-by-Step method is more effective at determining “When.” The Binary Search method provides a middle-ground performance.
 

– Hybrid Approaches Show Promise but at a High Cost: The researchers found that combining different methods, such as using the All-at-Once approach to identify a potential agent and then applying the Step-by-Step method to find the error, can improve overall performance. However, this comes with a significant increase in computational cost.

– State-of-the-Art Models Struggle: Surprisingly, even the most advanced reasoning models, like OpenAI o1 and DeepSeek R1, find this task challenging.- This h…

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🔗 Sumber: syncedreview.com