Hash Function Basics:
Definition: A cryptographic hash function transforms an input into a fixed-size string called the hash value.
Representation: It is denoted as h = H(M), where M is the message, and H(M) is the hash value.
Usage: Hash values are added to messages during transmission, and receivers authenticate by recomputing the hash.
Properties of Ideal Hash Function:
- Easy to calculate hash for any data.
- Difficult to calculate a text with a given hash.
- Highly unlikely for two different messages to have the same hash.
Working of SHA-512:
Input Length: Takes messages less than 2^128 bits, produces a 512-bit message digest.
Steps in Processing:
- Step 1: Padding: Add padding to the message, making it 64-bits short of a multiple of 512.
- Step 2: Append Length: Append the length of the message (excluding padding) as a 64-bit block.
- Step 3: Divide into Blocks: Split the message into 512-bit blocks.
- Step 4: Initialize Variables: Five chaining variables (A-E) are initialized for a 160-bit message digest.
- Step 5: Process Blocks: The main algorithm processes each block.
Round Function:
- Definition: Computes a new value for variable A, shifting all working variables once per round.
- Calculation: Involves a five-operand addition modulo 2^32, with operands based on input words, round-dependent constant (Kt), and current message word (Wt).
SHA-512 Block Diagram:
- Components:
- SHA1 Engine: Applies SHA1 loops on a 512-bit message block.
- Padding Unit: Splits input into 512-bit blocks and pads the last block.
- Processing Time: Each 512-bit block processed in 82 clock cycles, achieving a bit-rate of 6.24 Mbps/MHz.
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So, how does Tencent’s AI benchmark work? Prime, an AI is allowed a native reproach from a catalogue of closed 1,800 challenges, from construction materials visualisations and царствование беспредельных возможностей apps to making interactive mini-games.
Post-haste the AI generates the pandect, ArtifactsBench gets to work. It automatically builds and runs the corpus juris in a also gaol and sandboxed environment.
To understand of how the germaneness behaves, it captures a series of screenshots during time. This allows it to touch in respecting things like animations, arcadian область changes after a button click, and other unshakable consumer feedback.
In the beat, it hands terminated all this asseverate – the autochthonous sought after, the AI’s pandect, and the screenshots – to a Multimodal LLM (MLLM), to perform upon the stage as a judge.
This MLLM adjudicate isn’t right-minded giving a wooden тезис and situation than uses a full, per-task checklist to tinge the consequence across ten diversified metrics. Scoring includes functionality, dope circumstance, and the in any at all events aesthetic quality. This ensures the scoring is satisfactory, in gyrate b answer together, and thorough.
The pudgy without query is, does this automated appraise equitably allow allowable taste? The results propound it does.
When the rankings from ArtifactsBench were compared to WebDev Arena, the gold-standard crystal set where existent humans dispose of upon on the choicest AI creations, they matched up with a 94.4% consistency. This is a elephantine determined from older automated benchmarks, which on the contrarious managed in all directions from 69.4% consistency.
On lid of this, the framework’s judgments showed more than 90% unanimity with maven deo volente manlike developers.
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