Buy Crypto
Pay with
USD
USD
Buy & Sell
Hot
Supports Visa, Mastercard, SEPA & more
P2P
0 Fees
Flexible trading, zero fees
Gate Card
Use your crypto for payments worldwide
Markets
Trade
Basic
Advanced
Futures
Futures
Access hundreds of perpetual contracts
CFD
Gold
One platform for global traditional assets
Options
Hot
Trade European-style vanilla options
Unified Account
Maximize your capital efficiency
Demo Trading
Introduction to Futures Trading
Learn the basics of futures trading
Futures Events
Join events to earn rewards
Demo Trading
Use virtual funds to practice risk-free trading
U.S. Stocks
CFD
U.S. stock CFD derivatives
Futures
High leverage, 24/7 trading
Tokenized Stocks
Backed by real stock assets
GUSD
Mint GUSD for Treasury RWA yields
Earn
Launch
CandyDrop
tag
Collect candies to earn airdrops
Launchpool
Quick staking, earn potential new tokens
HODLer Airdrop
Hold GT and get massive airdrops for free
Pre-IPOs
Unlock full access to global stock IPOs
Alpha Points
Trade on-chain assets and earn airdrops
Futures Points
Earn futures points and claim airdrop rewards
Investment
Square
Square
Post, share, and explore crypto trends
Live
moments live
Live crypto market analysis
Chat
Chat with crypto traders
News
What is happening in crypto
flower_head
More
Promotions
AI
Others
TradFi
Rewards
Square
Latest
Hot
News
Profile

Berkeley AI emphasizes that understanding the reasons for failure is more important than benchmark scores.

MeNews
robot
Abstract generation in progress
ME News Report, April 19 (UTC+8), recently, researchers from Berkeley AI shared views from Dawn Song, emphasizing that when evaluating agents, understanding the specific reasons for their failures is more important than simply focusing on benchmark scores. The article suggests that long-horizon failures should be broken down into diagnosable patterns to enable more precise identification and analysis of where and why agents fail. The original text did not provide further details on specific benchmarks, analysis methods, or failure pattern classifications. (Source: InFoQ)
View Original
This page may contain third-party content, which is provided for information purposes only (not representations/warranties) and should not be considered as an endorsement of its views by Gate, nor as financial or professional advice. See Disclaimer for details.
  • Reward
  • 7
  • 2
  • Share
Comment
Add a comment
Add a comment
MildRugAllergy
· 3h ago
The term "long-term failure" is used accurately; short-term task success does not guarantee long-term reliability.
View OriginalReply0
RetroRadioWaves
· 3h ago
Decomposition failure modes sound simple, but in practice, implementing them likely involves a bunch of edge cases.
View OriginalReply0
NeonMint
· 4h ago
I feel that the community is now too focused on benchmark rankings; this kind of contrarian research is more valuable.
View OriginalReply0
ZenOfZK
· 4h ago
Berkeley AI has always been quite solid; looking forward to the specific methodology being made public.
View OriginalReply0
APuppyInTheWarmSun
· 4h ago
Agent evaluation really does need a new paradigm. The upper limit on accuracy is within reach, but robustness is the truly hard part.
View OriginalReply0
Can'tSleepWithoutSigningThe
· 4h ago
Dawn Song’s team has previously been quite meticulous about security, so this time it probably won’t be too abstract either.
View OriginalReply0
OracleBabysitter
· 4h ago
It's a pity that the details weren't provided in the original text; I want to see what the specific taxonomy looks like.
View OriginalReply0

  • Pinned

Sitemap