📌 MAROKO133 Eksklusif ai: Researchers from PSU and Duke introduce “Multi-Agent Sys
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Meet the author
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
2. failure-responsible agent and the decisive error step that led to the task’s failure.
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 m…
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🔗 Sumber: syncedreview.com
📌 MAROKO133 Hot ai: Baidu unveils proprietary ERNIE 5 beating GPT-5 performance on
Mere hours after OpenAI updated its flagship foundation model GPT-5 to GPT-5.1, promising reduced token usage overall and a more pleasant personality with more preset options, Chinese search giant Baidu unveiled its next-generation foundation model, ERNIE 5.0, alongside a suite of AI product upgrades and strategic international expansions.
The goal: to position as a global contender in the increasingly competitive enterprise AI market.
Announced at the company's Baidu World 2025 event, ERNIE 5.0 is a proprietary, natively omni-modal model designed to jointly process and generate content across text, images, audio, and video.
Unlike Baidu’s recently released ERNIE-4.5-VL-28B-A3B-Thinking, which is open source under an enterprise-friendly and permissive Apache 2.0 license, ERNIE 5.0 is a proprietary model and is available only via Baidu’s ERNIE Bot website (I needed to select it manuallyu from the model picker dropdown) and the Qianfan cloud platform application programming interface (API) for enterprise customers.
Alongside the model launch, Baidu introduced major updates to its digital human platform, no-code tools, and general-purpose AI agents — all targeted at expanding its AI footprint beyond China.
The company also introduced ERNIE 5.0 Preview 1022, a variant optimized for text-intensive tasks, alongside the general preview model that balances across modalities.
Baidu emphasized that ERNIE 5.0 represents a shift in how intelligence is deployed at scale, with CEO Robin Li stating: “When you internalize AI, it becomes a native capability and transforms intelligence from a cost into a source of productivity.”
Where ERNIE 5.0 outshines GPT-5 and Gemini 2.5 Pro
ERNIE 5.0’s benchmark results suggest that Baidu has achieved parity—or near-parity—with the top Western foundation models across a wide spectrum of tasks.
In public benchmark slides shared during the Baidu World 2025 event, ERNIE 5.0 Preview outperformed or matched OpenAI’s GPT-5-High and Google’s Gemini 2.5 Pro in multimodal reasoning, document understanding, and image-based QA, while also demonstrating strong language modeling and code execution abilities.
The company emphasized its ability to handle joint inputs and outputs across modalities, rather than relying on post-hoc modality fusion, which it framed as a technical differentiator.
On visual tasks, ERNIE 5.0 achieved leading scores on OCRBench, DocVQA, and ChartQA, three benchmarks that test document recognition, comprehension, and structured data reasoning.
Baidu claims the model beat both GPT-5-High and Gemini 2.5 Pro on these document and chart-based benchmarks, areas it describes as core to enterprise applications like automated document processing and financial analysis.
In image generation, ERNIE 5.0 tied or exceeded Google’s Veo3 across categories including semantic alignment and image quality, according to Baidu’s internal GenEval-based evaluation. Baidu claimed that the model’s multimodal integration allows it to generate and interpret visual content with greater contextual awareness than models relying on modality-specific encoders.
For audio and speech tasks, ERNIE 5.0 demonstrated competitive results on MM-AU and TUT2017 audio understanding benchmarks, as well as question answering from spoken language inputs. Its audio performance, while not as heavily emphasized as vision or text, suggests a broad capability footprint intended to support full-spectrum multimodal applications.
In language tasks, the model showed strong results on instruction following, factual question answering, and mathematical reasoning—core areas that define the enterprise utility of large language models.
The Preview 1022 variant of ERNIE 5.0, tailored for textual performance, showed even stronger language-specific results in early developer access. While Baidu does not claim broad superiority in general language reasoning, its internal evaluations suggest that ERNIE 5.0 Preview 1022 closes the gap with top-tier English-language models and outperforms them in Chinese-language performance.
While Baidu did not release full benchmark details or raw scores publicly, its performance positioning suggests a deliberate attempt to frame ERNIE 5.0 not as a niche multimodal system but as a flagship model competitive with the largest closed models in general-purpose reasoning.
Where Baidu claims a clear lead is in structured document understanding, visual chart reasoning, and integration of multiple modalities into a single, native modeling architecture. Independent verification of these results remains pending, but the breadth of claimed capabilities positions ERNIE 5.0 as a serious alternative in the multimodal foundation model landscape.
Enterprise Pricing Strategy
ERNIE 5.0 is positioned at the premium end of Baidu’s model pricing structure. The company has released specific pricing for API usage on its Qianfan platform, aligning the cost with other top-tier offerings from Chinese competitors like Alibaba.
Model
Input Cost (per 1K tokens)
Output Cost (per 1K tokens)
Source
ERNIE 5.0
$0.00085 (¥0.006)
$0.0034 (¥0.024)
ERNIE 4.5 Turbo (ex.)
$0.00011 (¥0.0008)
$0.00045 (¥0.0032)
Qwen3 (Coder ex.)
$0.00085 (¥0.006)
$0.0034 (¥0.024)
The contrast in cost between ERNIE 5.0 and earlier models such as ERNIE 4.5 Turbo underscores Baidu’s strategy to differentiate between high-volume, low-cost models and high-capability models designed for complex tasks and multimodal reasoning.
Compared to other U.S. alternatives, it remains mid-range in pricing:
Model
Input (/1 M tokens)
Output (/1 M tokens)
Source
GPT-5.1
$1.25
$10.00
ERNIE 5.0
$0.85
$3.40
ERNIE 4.5 Turbo (ex.)
$0.11
$0.45
Claude Opus 4.1
$15.00
$75.00
Gemini 2.5 Pro
$1.25 (≤200k) / $2.50 (>200k)
$10.00 (≤200k) / $15.00 (>200k)
Grok 4 (grok-4-0709)
$3.00
$15.00
<a href="https://docs.x.ai/docs/models/grok-4-0709?utm_sourc…
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🔗 Sumber: venturebeat.com
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